idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
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36,601 | Goodness of fit test on sparse contigency tables with high dimensionality | Instead of an exact test you can do a Chi-squared test based on Monte Carlo simulations. You would make N 2x3500 tables consisting of your "known distribution vector" and each of your N other vectors, and run N tests.
For each test, essentially the test is simulating a bunch of tables according to the null hypothesis ... | Goodness of fit test on sparse contigency tables with high dimensionality | Instead of an exact test you can do a Chi-squared test based on Monte Carlo simulations. You would make N 2x3500 tables consisting of your "known distribution vector" and each of your N other vectors, | Goodness of fit test on sparse contigency tables with high dimensionality
Instead of an exact test you can do a Chi-squared test based on Monte Carlo simulations. You would make N 2x3500 tables consisting of your "known distribution vector" and each of your N other vectors, and run N tests.
For each test, essentially ... | Goodness of fit test on sparse contigency tables with high dimensionality
Instead of an exact test you can do a Chi-squared test based on Monte Carlo simulations. You would make N 2x3500 tables consisting of your "known distribution vector" and each of your N other vectors, |
36,602 | Goodness of fit test on sparse contigency tables with high dimensionality | I assume that you want to test whether the number of occurences is independent of the county. In this case, may you can try the following:
First, transform your $1\times 3500$ tables to $2\times 3500$ tables (as in this post). Let $T$ be one of the $2\times 3500$ tables you have observed. A test on independence which ... | Goodness of fit test on sparse contigency tables with high dimensionality | I assume that you want to test whether the number of occurences is independent of the county. In this case, may you can try the following:
First, transform your $1\times 3500$ tables to $2\times 3500 | Goodness of fit test on sparse contigency tables with high dimensionality
I assume that you want to test whether the number of occurences is independent of the county. In this case, may you can try the following:
First, transform your $1\times 3500$ tables to $2\times 3500$ tables (as in this post). Let $T$ be one of ... | Goodness of fit test on sparse contigency tables with high dimensionality
I assume that you want to test whether the number of occurences is independent of the county. In this case, may you can try the following:
First, transform your $1\times 3500$ tables to $2\times 3500 |
36,603 | Goodness of fit test on sparse contigency tables with high dimensionality | Hmm... wouldn't you use something like the Gini coefficient (or its related methods). From my understanding they are developed for exactly this kind of situation | Goodness of fit test on sparse contigency tables with high dimensionality | Hmm... wouldn't you use something like the Gini coefficient (or its related methods). From my understanding they are developed for exactly this kind of situation | Goodness of fit test on sparse contigency tables with high dimensionality
Hmm... wouldn't you use something like the Gini coefficient (or its related methods). From my understanding they are developed for exactly this kind of situation | Goodness of fit test on sparse contigency tables with high dimensionality
Hmm... wouldn't you use something like the Gini coefficient (or its related methods). From my understanding they are developed for exactly this kind of situation |
36,604 | Probabilistic models for partial least squares, reduced rank regression, and canonical correlation analysis? | Probabilistic canonical correlation analysis (probabilistic CCA, PCCA) was introduced in Bach & Jordan, 2005, A Probabilistic Interpretation of
Canonical Correlation Analysis, several years after Tipping & Bishop presented their probabilistic principal component analysis (probabilistic PCA, PPCA).
Very briefly, it is ... | Probabilistic models for partial least squares, reduced rank regression, and canonical correlation a | Probabilistic canonical correlation analysis (probabilistic CCA, PCCA) was introduced in Bach & Jordan, 2005, A Probabilistic Interpretation of
Canonical Correlation Analysis, several years after Tipp | Probabilistic models for partial least squares, reduced rank regression, and canonical correlation analysis?
Probabilistic canonical correlation analysis (probabilistic CCA, PCCA) was introduced in Bach & Jordan, 2005, A Probabilistic Interpretation of
Canonical Correlation Analysis, several years after Tipping & Bisho... | Probabilistic models for partial least squares, reduced rank regression, and canonical correlation a
Probabilistic canonical correlation analysis (probabilistic CCA, PCCA) was introduced in Bach & Jordan, 2005, A Probabilistic Interpretation of
Canonical Correlation Analysis, several years after Tipp |
36,605 | Surrogate Time Series using Fourier Transform | You need to use the Fourier transform (and inverse transform) for real time series, i.e., rfft and irfft, respectively. This way you ensure that your surrogate is real. You can do this by replacing the respective lines of your code with the following:
ts_fourier = numpy.fft.rfft(ts)
random_phases = numpy.exp(numpy.ran... | Surrogate Time Series using Fourier Transform | You need to use the Fourier transform (and inverse transform) for real time series, i.e., rfft and irfft, respectively. This way you ensure that your surrogate is real. You can do this by replacing th | Surrogate Time Series using Fourier Transform
You need to use the Fourier transform (and inverse transform) for real time series, i.e., rfft and irfft, respectively. This way you ensure that your surrogate is real. You can do this by replacing the respective lines of your code with the following:
ts_fourier = numpy.ff... | Surrogate Time Series using Fourier Transform
You need to use the Fourier transform (and inverse transform) for real time series, i.e., rfft and irfft, respectively. This way you ensure that your surrogate is real. You can do this by replacing th |
36,606 | Why aren't Gaussian Activation Functions used more often in Neural Networks? | @Glen_b notes in comments that it's expensive to compute exact Normal CDF probabilities. Accurate approximations exist, but one still must ask Why do this?
Logit and probit functions look basically the same, up to rescaling.
(Image reproduced from @gung's answer here.)
From a practical standpoint, it's hard see how ... | Why aren't Gaussian Activation Functions used more often in Neural Networks? | @Glen_b notes in comments that it's expensive to compute exact Normal CDF probabilities. Accurate approximations exist, but one still must ask Why do this?
Logit and probit functions look basically t | Why aren't Gaussian Activation Functions used more often in Neural Networks?
@Glen_b notes in comments that it's expensive to compute exact Normal CDF probabilities. Accurate approximations exist, but one still must ask Why do this?
Logit and probit functions look basically the same, up to rescaling.
(Image reproduc... | Why aren't Gaussian Activation Functions used more often in Neural Networks?
@Glen_b notes in comments that it's expensive to compute exact Normal CDF probabilities. Accurate approximations exist, but one still must ask Why do this?
Logit and probit functions look basically t |
36,607 | Understanding weight distribution in neural network | This is expected. Weights in a CNN form feature detectors, so that a certain pattern in an image is connected to strong weights, but the rest of the image pixels should not cause any activations in the next layer neurons.
Only a small fraction of neurons in a layer is activated every time an image is shown, and a smal... | Understanding weight distribution in neural network | This is expected. Weights in a CNN form feature detectors, so that a certain pattern in an image is connected to strong weights, but the rest of the image pixels should not cause any activations in th | Understanding weight distribution in neural network
This is expected. Weights in a CNN form feature detectors, so that a certain pattern in an image is connected to strong weights, but the rest of the image pixels should not cause any activations in the next layer neurons.
Only a small fraction of neurons in a layer i... | Understanding weight distribution in neural network
This is expected. Weights in a CNN form feature detectors, so that a certain pattern in an image is connected to strong weights, but the rest of the image pixels should not cause any activations in th |
36,608 | Understanding weight distribution in neural network | This is a really good question and there can be multiple explanations to this phenomenon. I will try to give out a non-exhaustive list for the same.
Sparse data ? You mean sparse/binary feature set ? hmmm...this can happen. See in that case your network does not require much parameters to encode this function. Every n... | Understanding weight distribution in neural network | This is a really good question and there can be multiple explanations to this phenomenon. I will try to give out a non-exhaustive list for the same.
Sparse data ? You mean sparse/binary feature set ? | Understanding weight distribution in neural network
This is a really good question and there can be multiple explanations to this phenomenon. I will try to give out a non-exhaustive list for the same.
Sparse data ? You mean sparse/binary feature set ? hmmm...this can happen. See in that case your network does not requ... | Understanding weight distribution in neural network
This is a really good question and there can be multiple explanations to this phenomenon. I will try to give out a non-exhaustive list for the same.
Sparse data ? You mean sparse/binary feature set ? |
36,609 | Proving that cosine distance function defined by cosine similarity between two unit vectors does not satisfy triangle inequality | $$\text{cos-dist}(A, B) = 1 - \text{cos-sim}(A, B)$$
$$\text{cos-sim}(A, B) = \frac{\langle A, B \rangle}{||A|| \cdot ||B||} = \frac{\sum\limits_{i=1}^n A_i \cdot B_i}{\sqrt{\sum\limits_{i=1}^n A_i^2} \cdot \sqrt{\sum\limits_{i=1}^n B_i^2}}$$
Triangle inequality for cosine distance tooks a form of (of course it doesn't... | Proving that cosine distance function defined by cosine similarity between two unit vectors does not | $$\text{cos-dist}(A, B) = 1 - \text{cos-sim}(A, B)$$
$$\text{cos-sim}(A, B) = \frac{\langle A, B \rangle}{||A|| \cdot ||B||} = \frac{\sum\limits_{i=1}^n A_i \cdot B_i}{\sqrt{\sum\limits_{i=1}^n A_i^2} | Proving that cosine distance function defined by cosine similarity between two unit vectors does not satisfy triangle inequality
$$\text{cos-dist}(A, B) = 1 - \text{cos-sim}(A, B)$$
$$\text{cos-sim}(A, B) = \frac{\langle A, B \rangle}{||A|| \cdot ||B||} = \frac{\sum\limits_{i=1}^n A_i \cdot B_i}{\sqrt{\sum\limits_{i=1}... | Proving that cosine distance function defined by cosine similarity between two unit vectors does not
$$\text{cos-dist}(A, B) = 1 - \text{cos-sim}(A, B)$$
$$\text{cos-sim}(A, B) = \frac{\langle A, B \rangle}{||A|| \cdot ||B||} = \frac{\sum\limits_{i=1}^n A_i \cdot B_i}{\sqrt{\sum\limits_{i=1}^n A_i^2} |
36,610 | Two-sample Kolmogorov–Smirnov test with weights | I have found some information about how to modify the KS and AD tests for weighted samples in Numerical Methods of Statistics by Monohan, pg. 334 in 1E and pg. 358 in 2E. This Google Books link may show the relevant page. I have also attached a screenshot of the relevant page. | Two-sample Kolmogorov–Smirnov test with weights | I have found some information about how to modify the KS and AD tests for weighted samples in Numerical Methods of Statistics by Monohan, pg. 334 in 1E and pg. 358 in 2E. This Google Books link may sh | Two-sample Kolmogorov–Smirnov test with weights
I have found some information about how to modify the KS and AD tests for weighted samples in Numerical Methods of Statistics by Monohan, pg. 334 in 1E and pg. 358 in 2E. This Google Books link may show the relevant page. I have also attached a screenshot of the relevant ... | Two-sample Kolmogorov–Smirnov test with weights
I have found some information about how to modify the KS and AD tests for weighted samples in Numerical Methods of Statistics by Monohan, pg. 334 in 1E and pg. 358 in 2E. This Google Books link may sh |
36,611 | Concrete examples of a frequentist approach that is superior to a Bayesian one [closed] | A nice example would be a betting scenario where a frequentist and Bayesian bet against each other about some future outcome and the frequentist has positive expected value.
I will not give you this example because such an example would favor a Bayesian approach unless the Bayesian chooses a bad prior which is a cop-o... | Concrete examples of a frequentist approach that is superior to a Bayesian one [closed] | A nice example would be a betting scenario where a frequentist and Bayesian bet against each other about some future outcome and the frequentist has positive expected value.
I will not give you this | Concrete examples of a frequentist approach that is superior to a Bayesian one [closed]
A nice example would be a betting scenario where a frequentist and Bayesian bet against each other about some future outcome and the frequentist has positive expected value.
I will not give you this example because such an example ... | Concrete examples of a frequentist approach that is superior to a Bayesian one [closed]
A nice example would be a betting scenario where a frequentist and Bayesian bet against each other about some future outcome and the frequentist has positive expected value.
I will not give you this |
36,612 | Interpretation of mixed model output in lme4 and stan | The main question was the result of a bug in rstanarm that has since been fixed on GitHub.
However, in general, we do not recommend rstanarm models that exclude the intercept. A better alternative is to place a tight prior with mean zero on the intercept. In this case, including the intercept yields a better fit and si... | Interpretation of mixed model output in lme4 and stan | The main question was the result of a bug in rstanarm that has since been fixed on GitHub.
However, in general, we do not recommend rstanarm models that exclude the intercept. A better alternative is | Interpretation of mixed model output in lme4 and stan
The main question was the result of a bug in rstanarm that has since been fixed on GitHub.
However, in general, we do not recommend rstanarm models that exclude the intercept. A better alternative is to place a tight prior with mean zero on the intercept. In this ca... | Interpretation of mixed model output in lme4 and stan
The main question was the result of a bug in rstanarm that has since been fixed on GitHub.
However, in general, we do not recommend rstanarm models that exclude the intercept. A better alternative is |
36,613 | XG Boost vs Random Forest for Time Series Regression Forecasting | The easiest way to handle 'tuning' of the num_rounds parameter is to let XGBoost do it for you. You can set theearly_stopping_rounds parameter to n in the train method and the model will stop training once error hasn't decreased for n rounds.
See this example from the Liberty Mutual Kaggle Competition:
As noted in th... | XG Boost vs Random Forest for Time Series Regression Forecasting | The easiest way to handle 'tuning' of the num_rounds parameter is to let XGBoost do it for you. You can set theearly_stopping_rounds parameter to n in the train method and the model will stop trainin | XG Boost vs Random Forest for Time Series Regression Forecasting
The easiest way to handle 'tuning' of the num_rounds parameter is to let XGBoost do it for you. You can set theearly_stopping_rounds parameter to n in the train method and the model will stop training once error hasn't decreased for n rounds.
See this e... | XG Boost vs Random Forest for Time Series Regression Forecasting
The easiest way to handle 'tuning' of the num_rounds parameter is to let XGBoost do it for you. You can set theearly_stopping_rounds parameter to n in the train method and the model will stop trainin |
36,614 | Find the rotation between set of points | This can be done using the Kabsch Algorithm. The algorithm finds the best least-squares estimate for rotation of $RX-Y$ where $R$ is rotation matrix, $X$ and $Y$ are your target and source matrices with 2 rows and n columns.
In [1] it is shown that this problem can be solved using singular value decomposition. The algo... | Find the rotation between set of points | This can be done using the Kabsch Algorithm. The algorithm finds the best least-squares estimate for rotation of $RX-Y$ where $R$ is rotation matrix, $X$ and $Y$ are your target and source matrices wi | Find the rotation between set of points
This can be done using the Kabsch Algorithm. The algorithm finds the best least-squares estimate for rotation of $RX-Y$ where $R$ is rotation matrix, $X$ and $Y$ are your target and source matrices with 2 rows and n columns.
In [1] it is shown that this problem can be solved usin... | Find the rotation between set of points
This can be done using the Kabsch Algorithm. The algorithm finds the best least-squares estimate for rotation of $RX-Y$ where $R$ is rotation matrix, $X$ and $Y$ are your target and source matrices wi |
36,615 | Find the rotation between set of points | I've done this with an iterative optimum-search, and tested 2 versions.
I've taken the original arrays and centered them calling this arrays cSRC and cTAR .
Then I've done a loop with angles $\varphi$ between $0$ and $2 \pi$ , and for each angle I computed the error-criterion using the difference between the rotated $\... | Find the rotation between set of points | I've done this with an iterative optimum-search, and tested 2 versions.
I've taken the original arrays and centered them calling this arrays cSRC and cTAR .
Then I've done a loop with angles $\varphi$ | Find the rotation between set of points
I've done this with an iterative optimum-search, and tested 2 versions.
I've taken the original arrays and centered them calling this arrays cSRC and cTAR .
Then I've done a loop with angles $\varphi$ between $0$ and $2 \pi$ , and for each angle I computed the error-criterion usi... | Find the rotation between set of points
I've done this with an iterative optimum-search, and tested 2 versions.
I've taken the original arrays and centered them calling this arrays cSRC and cTAR .
Then I've done a loop with angles $\varphi$ |
36,616 | How to generate samples uniformly at random from multiple discrete variables subject to constraints? | Let $n_i$ denote the number of balls of color $C_i$. Also, let $m_i$ and $M_i$ denote the minimum and the maximum number of balls of color $C_i$, respectively. We want to sample $(n_1, \dots, n_I)$ uniformly at random subject to the following constraints:
$m_i \leq n_i \leq M_i$
$\sum_{i=1}^I n_i = N$
First of all, ... | How to generate samples uniformly at random from multiple discrete variables subject to constraints? | Let $n_i$ denote the number of balls of color $C_i$. Also, let $m_i$ and $M_i$ denote the minimum and the maximum number of balls of color $C_i$, respectively. We want to sample $(n_1, \dots, n_I)$ un | How to generate samples uniformly at random from multiple discrete variables subject to constraints?
Let $n_i$ denote the number of balls of color $C_i$. Also, let $m_i$ and $M_i$ denote the minimum and the maximum number of balls of color $C_i$, respectively. We want to sample $(n_1, \dots, n_I)$ uniformly at random s... | How to generate samples uniformly at random from multiple discrete variables subject to constraints?
Let $n_i$ denote the number of balls of color $C_i$. Also, let $m_i$ and $M_i$ denote the minimum and the maximum number of balls of color $C_i$, respectively. We want to sample $(n_1, \dots, n_I)$ un |
36,617 | How to generate samples uniformly at random from multiple discrete variables subject to constraints? | Let's consider a generalization of this problem. There are $m=4$ cans of paint of $m=4$ distinct colors and $n^{(0)}=100$ balls. Can $i$ can hold up to $a^{(0)}_i = (100, 100, 50, 75)$ balls. You wish to generate configurations of balls in the cans having at least $b_i = (0, 50, 0, 25)$ balls in can $i$ for each $i$... | How to generate samples uniformly at random from multiple discrete variables subject to constraints? | Let's consider a generalization of this problem. There are $m=4$ cans of paint of $m=4$ distinct colors and $n^{(0)}=100$ balls. Can $i$ can hold up to $a^{(0)}_i = (100, 100, 50, 75)$ balls. You w | How to generate samples uniformly at random from multiple discrete variables subject to constraints?
Let's consider a generalization of this problem. There are $m=4$ cans of paint of $m=4$ distinct colors and $n^{(0)}=100$ balls. Can $i$ can hold up to $a^{(0)}_i = (100, 100, 50, 75)$ balls. You wish to generate con... | How to generate samples uniformly at random from multiple discrete variables subject to constraints?
Let's consider a generalization of this problem. There are $m=4$ cans of paint of $m=4$ distinct colors and $n^{(0)}=100$ balls. Can $i$ can hold up to $a^{(0)}_i = (100, 100, 50, 75)$ balls. You w |
36,618 | Fisher information for $\rho$ in a bivariate normal distribution | The OP clarified in a comment that he examines the standard bivariate normal distribution, with means and variances fixed to zero and unity correspondingly,
$$f(x,y) = \frac{1}{2 \pi \sqrt{1-\rho^2}}
\exp\left\{-\frac{x^2 +y^2 -2\rho xy}{2(1-\rho^2)}\right\} $$
In turn, this makes the distribution a member of ... | Fisher information for $\rho$ in a bivariate normal distribution | The OP clarified in a comment that he examines the standard bivariate normal distribution, with means and variances fixed to zero and unity correspondingly,
$$f(x,y) = \frac{1}{2 \pi \sqrt{1-\rho^2 | Fisher information for $\rho$ in a bivariate normal distribution
The OP clarified in a comment that he examines the standard bivariate normal distribution, with means and variances fixed to zero and unity correspondingly,
$$f(x,y) = \frac{1}{2 \pi \sqrt{1-\rho^2}}
\exp\left\{-\frac{x^2 +y^2 -2\rho xy}{2(1-\rho... | Fisher information for $\rho$ in a bivariate normal distribution
The OP clarified in a comment that he examines the standard bivariate normal distribution, with means and variances fixed to zero and unity correspondingly,
$$f(x,y) = \frac{1}{2 \pi \sqrt{1-\rho^2 |
36,619 | Uncertainty in random forest imputations from R missForest package | You're correct that understatements of imputation uncertainty is the reason that people use multiple imputation packages like MICE. I can't answer this question for the missForest example in particular, except to observe that by the same token that one might use multiple imputation in estimating a model, I see no reaso... | Uncertainty in random forest imputations from R missForest package | You're correct that understatements of imputation uncertainty is the reason that people use multiple imputation packages like MICE. I can't answer this question for the missForest example in particula | Uncertainty in random forest imputations from R missForest package
You're correct that understatements of imputation uncertainty is the reason that people use multiple imputation packages like MICE. I can't answer this question for the missForest example in particular, except to observe that by the same token that one ... | Uncertainty in random forest imputations from R missForest package
You're correct that understatements of imputation uncertainty is the reason that people use multiple imputation packages like MICE. I can't answer this question for the missForest example in particula |
36,620 | Uncertainty in random forest imputations from R missForest package | Using the mice package, you can conduct multiple imputation using random forest as the underlying modeling method. Modifying the example shown in the mice documentation:
imp <- mice(nhanes,defaultMethod=c("rf","rf","rf"))
This would create 5 imputed datasets, with all missing data, whether continuous or categorical, f... | Uncertainty in random forest imputations from R missForest package | Using the mice package, you can conduct multiple imputation using random forest as the underlying modeling method. Modifying the example shown in the mice documentation:
imp <- mice(nhanes,defaultMeth | Uncertainty in random forest imputations from R missForest package
Using the mice package, you can conduct multiple imputation using random forest as the underlying modeling method. Modifying the example shown in the mice documentation:
imp <- mice(nhanes,defaultMethod=c("rf","rf","rf"))
This would create 5 imputed da... | Uncertainty in random forest imputations from R missForest package
Using the mice package, you can conduct multiple imputation using random forest as the underlying modeling method. Modifying the example shown in the mice documentation:
imp <- mice(nhanes,defaultMeth |
36,621 | Uncertainty in random forest imputations from R missForest package | In the paper presenting the missForest package (see Ref below), it is mentioned in the abstract that: "By averaging over many unpruned classification or regression trees, random forest intrinsically constitutes a multiple imputation scheme."
...followed by:"Using the built-in out-of-bag error estimates of random forest... | Uncertainty in random forest imputations from R missForest package | In the paper presenting the missForest package (see Ref below), it is mentioned in the abstract that: "By averaging over many unpruned classification or regression trees, random forest intrinsically c | Uncertainty in random forest imputations from R missForest package
In the paper presenting the missForest package (see Ref below), it is mentioned in the abstract that: "By averaging over many unpruned classification or regression trees, random forest intrinsically constitutes a multiple imputation scheme."
...followed... | Uncertainty in random forest imputations from R missForest package
In the paper presenting the missForest package (see Ref below), it is mentioned in the abstract that: "By averaging over many unpruned classification or regression trees, random forest intrinsically c |
36,622 | Ethics of publishing covariate matrix | Yes, there is no way to reverse engineer the information. But, the ethics of data remain as is, and one needs to respect them before sharing information which has been mined from that data.
So, you need to take written permission from the lab which have done the experiments. If it is your lab, then include the permissi... | Ethics of publishing covariate matrix | Yes, there is no way to reverse engineer the information. But, the ethics of data remain as is, and one needs to respect them before sharing information which has been mined from that data.
So, you ne | Ethics of publishing covariate matrix
Yes, there is no way to reverse engineer the information. But, the ethics of data remain as is, and one needs to respect them before sharing information which has been mined from that data.
So, you need to take written permission from the lab which have done the experiments. If it ... | Ethics of publishing covariate matrix
Yes, there is no way to reverse engineer the information. But, the ethics of data remain as is, and one needs to respect them before sharing information which has been mined from that data.
So, you ne |
36,623 | Relation between linear regression prediction accuracy and correlation | In a linear regression you are trying to find the best fit of a set of output $Y=(Y_1,\dots,Y_n)$ as a linear function of input $X=(X_1,\dots,X_n)$, i.e. you want the $a$ and $b$ that gives the smallest residuals:
$$(a,b)= arg \min \sum_i (Y_i-aX_i-b)^2$$
if it is what you did and you formally get (if you are still w... | Relation between linear regression prediction accuracy and correlation | In a linear regression you are trying to find the best fit of a set of output $Y=(Y_1,\dots,Y_n)$ as a linear function of input $X=(X_1,\dots,X_n)$, i.e. you want the $a$ and $b$ that gives the smalle | Relation between linear regression prediction accuracy and correlation
In a linear regression you are trying to find the best fit of a set of output $Y=(Y_1,\dots,Y_n)$ as a linear function of input $X=(X_1,\dots,X_n)$, i.e. you want the $a$ and $b$ that gives the smallest residuals:
$$(a,b)= arg \min \sum_i (Y_i-aX_i... | Relation between linear regression prediction accuracy and correlation
In a linear regression you are trying to find the best fit of a set of output $Y=(Y_1,\dots,Y_n)$ as a linear function of input $X=(X_1,\dots,X_n)$, i.e. you want the $a$ and $b$ that gives the smalle |
36,624 | Relation between linear regression prediction accuracy and correlation | I think I know what's going on. Intuitiviely, your method of calculating prediction error seems OK - You take the residual value (i.e error) and see how big it is relative to your data (i.e., relative to either the predicted value or the value of your dependent variable) by dividing by that value. However, there's at l... | Relation between linear regression prediction accuracy and correlation | I think I know what's going on. Intuitiviely, your method of calculating prediction error seems OK - You take the residual value (i.e error) and see how big it is relative to your data (i.e., relative | Relation between linear regression prediction accuracy and correlation
I think I know what's going on. Intuitiviely, your method of calculating prediction error seems OK - You take the residual value (i.e error) and see how big it is relative to your data (i.e., relative to either the predicted value or the value of yo... | Relation between linear regression prediction accuracy and correlation
I think I know what's going on. Intuitiviely, your method of calculating prediction error seems OK - You take the residual value (i.e error) and see how big it is relative to your data (i.e., relative |
36,625 | Is it legitimate to refit my best model with my test data as a final step in the model building process | Yes,
Of course, the accuracy you should report is from training with the test set held out. But now it's time to make predictions and you want them to be as good as they can be. This is certainly legitimate and important for exactly the reasons you mention. Your most recent data is what you want to test against and ... | Is it legitimate to refit my best model with my test data as a final step in the model building proc | Yes,
Of course, the accuracy you should report is from training with the test set held out. But now it's time to make predictions and you want them to be as good as they can be. This is certainly le | Is it legitimate to refit my best model with my test data as a final step in the model building process
Yes,
Of course, the accuracy you should report is from training with the test set held out. But now it's time to make predictions and you want them to be as good as they can be. This is certainly legitimate and imp... | Is it legitimate to refit my best model with my test data as a final step in the model building proc
Yes,
Of course, the accuracy you should report is from training with the test set held out. But now it's time to make predictions and you want them to be as good as they can be. This is certainly le |
36,626 | Is it legitimate to refit my best model with my test data as a final step in the model building process | It depends on the model you're using. Unless you're limiting yourself to a simple class of convex models/loss functions, you're considerably better off keeping a final test split. Here's why:
Let's say you collect iid sample pairs from your data generating distribution, some set of (x, y). You then split this up into a... | Is it legitimate to refit my best model with my test data as a final step in the model building proc | It depends on the model you're using. Unless you're limiting yourself to a simple class of convex models/loss functions, you're considerably better off keeping a final test split. Here's why:
Let's sa | Is it legitimate to refit my best model with my test data as a final step in the model building process
It depends on the model you're using. Unless you're limiting yourself to a simple class of convex models/loss functions, you're considerably better off keeping a final test split. Here's why:
Let's say you collect ii... | Is it legitimate to refit my best model with my test data as a final step in the model building proc
It depends on the model you're using. Unless you're limiting yourself to a simple class of convex models/loss functions, you're considerably better off keeping a final test split. Here's why:
Let's sa |
36,627 | Truncated power basis function and continuity in b-splines | Truncated power basis functions can be used to construct splines, the purpose of which is to interpolate or smooth data.
B-splines are an alternative system to construct splines. They are different algorithms - 2 separate systems for achieving the same goal. B-splines are more commonly used because, while the truncated... | Truncated power basis function and continuity in b-splines | Truncated power basis functions can be used to construct splines, the purpose of which is to interpolate or smooth data.
B-splines are an alternative system to construct splines. They are different al | Truncated power basis function and continuity in b-splines
Truncated power basis functions can be used to construct splines, the purpose of which is to interpolate or smooth data.
B-splines are an alternative system to construct splines. They are different algorithms - 2 separate systems for achieving the same goal. B-... | Truncated power basis function and continuity in b-splines
Truncated power basis functions can be used to construct splines, the purpose of which is to interpolate or smooth data.
B-splines are an alternative system to construct splines. They are different al |
36,628 | Truncated power basis function and continuity in b-splines | I don't think that the truncating power basis function is added for continuity. The main advantage of the truncated power function basis is the simplicity of its construction and the ease of interpreting the parameters in a model that corresponds to these basis functions. But it can definitely be proved that adding tru... | Truncated power basis function and continuity in b-splines | I don't think that the truncating power basis function is added for continuity. The main advantage of the truncated power function basis is the simplicity of its construction and the ease of interpret | Truncated power basis function and continuity in b-splines
I don't think that the truncating power basis function is added for continuity. The main advantage of the truncated power function basis is the simplicity of its construction and the ease of interpreting the parameters in a model that corresponds to these basis... | Truncated power basis function and continuity in b-splines
I don't think that the truncating power basis function is added for continuity. The main advantage of the truncated power function basis is the simplicity of its construction and the ease of interpret |
36,629 | Q-Learning stuck on a nearly toy problem | Exploration might be an issue. Are you sure the algorithm tries all legal actions during the training? Setting a very high initial estimate for all the Q-values will encourage exploration at the start of the training. You could also try "soft selection" where you randomly select an action other than the one with highes... | Q-Learning stuck on a nearly toy problem | Exploration might be an issue. Are you sure the algorithm tries all legal actions during the training? Setting a very high initial estimate for all the Q-values will encourage exploration at the start | Q-Learning stuck on a nearly toy problem
Exploration might be an issue. Are you sure the algorithm tries all legal actions during the training? Setting a very high initial estimate for all the Q-values will encourage exploration at the start of the training. You could also try "soft selection" where you randomly select... | Q-Learning stuck on a nearly toy problem
Exploration might be an issue. Are you sure the algorithm tries all legal actions during the training? Setting a very high initial estimate for all the Q-values will encourage exploration at the start |
36,630 | Handling case weight in the Random Forest packages in R | Do not duplicate to up-weight samples. That would make the out-of-bag cross validation very over optimistic.
Both stratification and class weighting are implemented in randomForest and here's some other threads on that.
random-forest-with-classes-that-are-very-unbalanced
R package for Weighted Random Forest? classwt op... | Handling case weight in the Random Forest packages in R | Do not duplicate to up-weight samples. That would make the out-of-bag cross validation very over optimistic.
Both stratification and class weighting are implemented in randomForest and here's some oth | Handling case weight in the Random Forest packages in R
Do not duplicate to up-weight samples. That would make the out-of-bag cross validation very over optimistic.
Both stratification and class weighting are implemented in randomForest and here's some other threads on that.
random-forest-with-classes-that-are-very-unb... | Handling case weight in the Random Forest packages in R
Do not duplicate to up-weight samples. That would make the out-of-bag cross validation very over optimistic.
Both stratification and class weighting are implemented in randomForest and here's some oth |
36,631 | Handling case weight in the Random Forest packages in R | Replicating your observations might be a good idea. I know that WEKA allows different weights for each instance.
From WEKA's wiki:
This feature exists in versions of Weka >= 3.5.8.
A weight can be associated with an instance in a standard ARFF file by appending it to the end of the line for that instance and enclosing... | Handling case weight in the Random Forest packages in R | Replicating your observations might be a good idea. I know that WEKA allows different weights for each instance.
From WEKA's wiki:
This feature exists in versions of Weka >= 3.5.8.
A weight can be as | Handling case weight in the Random Forest packages in R
Replicating your observations might be a good idea. I know that WEKA allows different weights for each instance.
From WEKA's wiki:
This feature exists in versions of Weka >= 3.5.8.
A weight can be associated with an instance in a standard ARFF file by appending i... | Handling case weight in the Random Forest packages in R
Replicating your observations might be a good idea. I know that WEKA allows different weights for each instance.
From WEKA's wiki:
This feature exists in versions of Weka >= 3.5.8.
A weight can be as |
36,632 | variational inference with KL | The KL divergences can be seen as a product of a weighting function $w(x)$ and a penalty function $g(x)$, i.e. $KL(q||p) = \sum_x w(x)g(x)$ with $w(x) = q(x)$ and $g(x) = \log\frac{q(x)}{p(x)}$ in the case of the reverse KL divergence. Whenever the weighting function is close to zero, i.e. $w(x) \approx 0$, the product... | variational inference with KL | The KL divergences can be seen as a product of a weighting function $w(x)$ and a penalty function $g(x)$, i.e. $KL(q||p) = \sum_x w(x)g(x)$ with $w(x) = q(x)$ and $g(x) = \log\frac{q(x)}{p(x)}$ in the | variational inference with KL
The KL divergences can be seen as a product of a weighting function $w(x)$ and a penalty function $g(x)$, i.e. $KL(q||p) = \sum_x w(x)g(x)$ with $w(x) = q(x)$ and $g(x) = \log\frac{q(x)}{p(x)}$ in the case of the reverse KL divergence. Whenever the weighting function is close to zero, i.e.... | variational inference with KL
The KL divergences can be seen as a product of a weighting function $w(x)$ and a penalty function $g(x)$, i.e. $KL(q||p) = \sum_x w(x)g(x)$ with $w(x) = q(x)$ and $g(x) = \log\frac{q(x)}{p(x)}$ in the |
36,633 | Why use precision instead of variance in a prior? | I guess that you ask about parametrization of Normal distribution, as some statistical software such as BUGS, or JAGS use precision rather than variance. First, notice that it is not true for every statistical package, e.g. R or Stan use "traditional" standard deviation (square root of variance), so this is not always ... | Why use precision instead of variance in a prior? | I guess that you ask about parametrization of Normal distribution, as some statistical software such as BUGS, or JAGS use precision rather than variance. First, notice that it is not true for every st | Why use precision instead of variance in a prior?
I guess that you ask about parametrization of Normal distribution, as some statistical software such as BUGS, or JAGS use precision rather than variance. First, notice that it is not true for every statistical package, e.g. R or Stan use "traditional" standard deviation... | Why use precision instead of variance in a prior?
I guess that you ask about parametrization of Normal distribution, as some statistical software such as BUGS, or JAGS use precision rather than variance. First, notice that it is not true for every st |
36,634 | Statistical test for increasing incidence of a rare event | You can use the non-parametric Mann-Kendall test. For this sample data, cases and the one-sided null hypothesis that there is no increasing trend, you can implement as follows in r.
> n<-length(cases)
> d<-outer(cases,cases,"-")
> s<-sum(sign(d[lower.tri(d)]))
> ties<-table(cases)
> v<-1/18*(n*(n-1)*(2*n+5)-sum(ties*(t... | Statistical test for increasing incidence of a rare event | You can use the non-parametric Mann-Kendall test. For this sample data, cases and the one-sided null hypothesis that there is no increasing trend, you can implement as follows in r.
> n<-length(cases) | Statistical test for increasing incidence of a rare event
You can use the non-parametric Mann-Kendall test. For this sample data, cases and the one-sided null hypothesis that there is no increasing trend, you can implement as follows in r.
> n<-length(cases)
> d<-outer(cases,cases,"-")
> s<-sum(sign(d[lower.tri(d)]))
>... | Statistical test for increasing incidence of a rare event
You can use the non-parametric Mann-Kendall test. For this sample data, cases and the one-sided null hypothesis that there is no increasing trend, you can implement as follows in r.
> n<-length(cases) |
36,635 | Statistical test for increasing incidence of a rare event | You could fit a very simple regression model consisting only of an intercept and time component and test the "significance" of the time component. For instance, you might model $Y_t \sim$ Poisson$(\lambda_t)$ where $Y_t$ is the number of occurences in year $t$ and $\log(\lambda_t) = \alpha + \beta t$ and check if $\be... | Statistical test for increasing incidence of a rare event | You could fit a very simple regression model consisting only of an intercept and time component and test the "significance" of the time component. For instance, you might model $Y_t \sim$ Poisson$(\l | Statistical test for increasing incidence of a rare event
You could fit a very simple regression model consisting only of an intercept and time component and test the "significance" of the time component. For instance, you might model $Y_t \sim$ Poisson$(\lambda_t)$ where $Y_t$ is the number of occurences in year $t$ ... | Statistical test for increasing incidence of a rare event
You could fit a very simple regression model consisting only of an intercept and time component and test the "significance" of the time component. For instance, you might model $Y_t \sim$ Poisson$(\l |
36,636 | Statistical test for increasing incidence of a rare event | Just check whether your number of new cases (i.e., number_affected) is significantly correlated with time (i.e., year). As any possible linear dependence of the event rate is at least distorted to the observational discretisation, you want to use a rank-based correlation coefficient, e.g., Kendall’s τ or Spearman’s ρ. | Statistical test for increasing incidence of a rare event | Just check whether your number of new cases (i.e., number_affected) is significantly correlated with time (i.e., year). As any possible linear dependence of the event rate is at least distorted to the | Statistical test for increasing incidence of a rare event
Just check whether your number of new cases (i.e., number_affected) is significantly correlated with time (i.e., year). As any possible linear dependence of the event rate is at least distorted to the observational discretisation, you want to use a rank-based co... | Statistical test for increasing incidence of a rare event
Just check whether your number of new cases (i.e., number_affected) is significantly correlated with time (i.e., year). As any possible linear dependence of the event rate is at least distorted to the |
36,637 | Should I bother that t-test power is relatively small when H0 is already rejected? | In a narrow sense, you are correct. Power is the chance to correctly reject a false null hypothesis, so you had a small chance but were able to do it anyway.
However, from a bayesian belief updating perspective, "reducing power implies reducing the belief shift that is warranted from observing a statistically significa... | Should I bother that t-test power is relatively small when H0 is already rejected? | In a narrow sense, you are correct. Power is the chance to correctly reject a false null hypothesis, so you had a small chance but were able to do it anyway.
However, from a bayesian belief updating p | Should I bother that t-test power is relatively small when H0 is already rejected?
In a narrow sense, you are correct. Power is the chance to correctly reject a false null hypothesis, so you had a small chance but were able to do it anyway.
However, from a bayesian belief updating perspective, "reducing power implies r... | Should I bother that t-test power is relatively small when H0 is already rejected?
In a narrow sense, you are correct. Power is the chance to correctly reject a false null hypothesis, so you had a small chance but were able to do it anyway.
However, from a bayesian belief updating p |
36,638 | Should I bother that t-test power is relatively small when H0 is already rejected? | It depends on how the power analysis was conducted. Generally speaking, there is a relationship between four variables (alpha, power, the effect size, and $N$) such that if you stipulate any three you can solve for the fourth.
Typically, when people run a power analysis after analyzing their data, they do a post-hoc... | Should I bother that t-test power is relatively small when H0 is already rejected? | It depends on how the power analysis was conducted. Generally speaking, there is a relationship between four variables (alpha, power, the effect size, and $N$) such that if you stipulate any three yo | Should I bother that t-test power is relatively small when H0 is already rejected?
It depends on how the power analysis was conducted. Generally speaking, there is a relationship between four variables (alpha, power, the effect size, and $N$) such that if you stipulate any three you can solve for the fourth.
Typical... | Should I bother that t-test power is relatively small when H0 is already rejected?
It depends on how the power analysis was conducted. Generally speaking, there is a relationship between four variables (alpha, power, the effect size, and $N$) such that if you stipulate any three yo |
36,639 | Should I bother that t-test power is relatively small when H0 is already rejected? | Denote $p$ the $p$-value of your test (as a random variable)
and fix some $\alpha$.
Call a test result significant or positive when $p \leq \alpha$.
We have $P(p \leq \alpha \,|\, H_0) \leq \alpha$.
Moreover, let $\beta$ be such that $P(p > \alpha \,|\, H_1) \leq \beta$.
Then $1-\beta$ is the power of the test.
Treatin... | Should I bother that t-test power is relatively small when H0 is already rejected? | Denote $p$ the $p$-value of your test (as a random variable)
and fix some $\alpha$.
Call a test result significant or positive when $p \leq \alpha$.
We have $P(p \leq \alpha \,|\, H_0) \leq \alpha$.
M | Should I bother that t-test power is relatively small when H0 is already rejected?
Denote $p$ the $p$-value of your test (as a random variable)
and fix some $\alpha$.
Call a test result significant or positive when $p \leq \alpha$.
We have $P(p \leq \alpha \,|\, H_0) \leq \alpha$.
Moreover, let $\beta$ be such that $P(... | Should I bother that t-test power is relatively small when H0 is already rejected?
Denote $p$ the $p$-value of your test (as a random variable)
and fix some $\alpha$.
Call a test result significant or positive when $p \leq \alpha$.
We have $P(p \leq \alpha \,|\, H_0) \leq \alpha$.
M |
36,640 | Elements of Statistical Learning alternatives | I agree that An Intro to Statistical Learning has a very accommodating tone. You may want to look at Learning From Data, A Short Course by Yaser Abu-Mostafa et al. I found this book and the accompanying youtube videos to be great.
Lastly, spdrnl's comment about Applied Predictive Modeling by Kuhn is a good suggestion.... | Elements of Statistical Learning alternatives | I agree that An Intro to Statistical Learning has a very accommodating tone. You may want to look at Learning From Data, A Short Course by Yaser Abu-Mostafa et al. I found this book and the accompanyi | Elements of Statistical Learning alternatives
I agree that An Intro to Statistical Learning has a very accommodating tone. You may want to look at Learning From Data, A Short Course by Yaser Abu-Mostafa et al. I found this book and the accompanying youtube videos to be great.
Lastly, spdrnl's comment about Applied Pre... | Elements of Statistical Learning alternatives
I agree that An Intro to Statistical Learning has a very accommodating tone. You may want to look at Learning From Data, A Short Course by Yaser Abu-Mostafa et al. I found this book and the accompanyi |
36,641 | Elements of Statistical Learning alternatives | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
Possible alternatives:
Pattern Recognition and Machin... | Elements of Statistical Learning alternatives | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| Elements of Statistical Learning alternatives
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
Possible... | Elements of Statistical Learning alternatives
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
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36,642 | What is the best algorithm to find similar text documents? | To find similar documents in very large document sets is locality sensitive hashing (LSH). An indication of the gist is: "One general approach to LSH is to “hash” items several times, in such a way that similar items are more likely to be hashed to the same bucket than dissimilar items are."
For in-depth information s... | What is the best algorithm to find similar text documents? | To find similar documents in very large document sets is locality sensitive hashing (LSH). An indication of the gist is: "One general approach to LSH is to “hash” items several times, in such a way th | What is the best algorithm to find similar text documents?
To find similar documents in very large document sets is locality sensitive hashing (LSH). An indication of the gist is: "One general approach to LSH is to “hash” items several times, in such a way that similar items are more likely to be hashed to the same buc... | What is the best algorithm to find similar text documents?
To find similar documents in very large document sets is locality sensitive hashing (LSH). An indication of the gist is: "One general approach to LSH is to “hash” items several times, in such a way th |
36,643 | What is the best algorithm to find similar text documents? | Why cluster? Why LDA?Much too expensive, complicated, and unreliable. Let me present you an incredible novel technique to find similar documents:
text search
There is a wonderful tool called Apache Lucene, that makes text search fast and easy to integrate, too. | What is the best algorithm to find similar text documents? | Why cluster? Why LDA?Much too expensive, complicated, and unreliable. Let me present you an incredible novel technique to find similar documents:
text search
There is a wonderful tool called Apache Lu | What is the best algorithm to find similar text documents?
Why cluster? Why LDA?Much too expensive, complicated, and unreliable. Let me present you an incredible novel technique to find similar documents:
text search
There is a wonderful tool called Apache Lucene, that makes text search fast and easy to integrate, too. | What is the best algorithm to find similar text documents?
Why cluster? Why LDA?Much too expensive, complicated, and unreliable. Let me present you an incredible novel technique to find similar documents:
text search
There is a wonderful tool called Apache Lu |
36,644 | What is the best algorithm to find similar text documents? | One good technique that I've seen used in the past for information retrieval applications is to shingle your target documents and query document, and then take the Jaccard similarity over sets of shingles.
Shingling is a procedure that takes a sliding window over characters in your document, representing a string as th... | What is the best algorithm to find similar text documents? | One good technique that I've seen used in the past for information retrieval applications is to shingle your target documents and query document, and then take the Jaccard similarity over sets of shin | What is the best algorithm to find similar text documents?
One good technique that I've seen used in the past for information retrieval applications is to shingle your target documents and query document, and then take the Jaccard similarity over sets of shingles.
Shingling is a procedure that takes a sliding window ov... | What is the best algorithm to find similar text documents?
One good technique that I've seen used in the past for information retrieval applications is to shingle your target documents and query document, and then take the Jaccard similarity over sets of shin |
36,645 | What is the best algorithm to find similar text documents? | This program uses word frequency analysis ("bag of words") and sorts the repository texts by Euclidian distance to the vector of frequencies of the sample text.
Given a sample text, this program lists the repository texts sorted by similarity: simple implementation of bag of words in C++. The algorithm is linear in the... | What is the best algorithm to find similar text documents? | This program uses word frequency analysis ("bag of words") and sorts the repository texts by Euclidian distance to the vector of frequencies of the sample text.
Given a sample text, this program lists | What is the best algorithm to find similar text documents?
This program uses word frequency analysis ("bag of words") and sorts the repository texts by Euclidian distance to the vector of frequencies of the sample text.
Given a sample text, this program lists the repository texts sorted by similarity: simple implementa... | What is the best algorithm to find similar text documents?
This program uses word frequency analysis ("bag of words") and sorts the repository texts by Euclidian distance to the vector of frequencies of the sample text.
Given a sample text, this program lists |
36,646 | Why (and when) does one have to learn the reward function from samples in reinforcement learning? | In his notes, when you must "estimate them from data", he does not mean the reward function. You rarely estimate the reward function. You typically learn the value function, which estimates the immediate reward plus the temporally-discounted future reward (if the temporal discount is zero, then you are estimating th... | Why (and when) does one have to learn the reward function from samples in reinforcement learning? | In his notes, when you must "estimate them from data", he does not mean the reward function. You rarely estimate the reward function. You typically learn the value function, which estimates the imme | Why (and when) does one have to learn the reward function from samples in reinforcement learning?
In his notes, when you must "estimate them from data", he does not mean the reward function. You rarely estimate the reward function. You typically learn the value function, which estimates the immediate reward plus the ... | Why (and when) does one have to learn the reward function from samples in reinforcement learning?
In his notes, when you must "estimate them from data", he does not mean the reward function. You rarely estimate the reward function. You typically learn the value function, which estimates the imme |
36,647 | Why (and when) does one have to learn the reward function from samples in reinforcement learning? | I agree with the other answers that usually, the reward function is not learned directly.
The transition probabilities also don't have to be learned. The agent can learn directly the action values, or even directly the policy, with policy gradien method for instance.
There are, however techniques for which the reward a... | Why (and when) does one have to learn the reward function from samples in reinforcement learning? | I agree with the other answers that usually, the reward function is not learned directly.
The transition probabilities also don't have to be learned. The agent can learn directly the action values, or | Why (and when) does one have to learn the reward function from samples in reinforcement learning?
I agree with the other answers that usually, the reward function is not learned directly.
The transition probabilities also don't have to be learned. The agent can learn directly the action values, or even directly the pol... | Why (and when) does one have to learn the reward function from samples in reinforcement learning?
I agree with the other answers that usually, the reward function is not learned directly.
The transition probabilities also don't have to be learned. The agent can learn directly the action values, or |
36,648 | Why (and when) does one have to learn the reward function from samples in reinforcement learning? | This is a good question which is deeper than any imprecise wording in Andrew's notes. It is true that in RL you generally do not need to learn a reward function R(S,A,S'); you need to specify it as part of the problem setup. BUT, there are algorithms (in my experience associated with the afterstate or post-decision sta... | Why (and when) does one have to learn the reward function from samples in reinforcement learning? | This is a good question which is deeper than any imprecise wording in Andrew's notes. It is true that in RL you generally do not need to learn a reward function R(S,A,S'); you need to specify it as pa | Why (and when) does one have to learn the reward function from samples in reinforcement learning?
This is a good question which is deeper than any imprecise wording in Andrew's notes. It is true that in RL you generally do not need to learn a reward function R(S,A,S'); you need to specify it as part of the problem setu... | Why (and when) does one have to learn the reward function from samples in reinforcement learning?
This is a good question which is deeper than any imprecise wording in Andrew's notes. It is true that in RL you generally do not need to learn a reward function R(S,A,S'); you need to specify it as pa |
36,649 | Why (and when) does one have to learn the reward function from samples in reinforcement learning? | In reinforcement learning, the agent learns the value function, not the reward function. The value of an action is it's overall utility; for example, an action may bring a high reward, but lead to low-value states, making it low-value.
What the quote says is that sometimes the world is complicated: some problems have s... | Why (and when) does one have to learn the reward function from samples in reinforcement learning? | In reinforcement learning, the agent learns the value function, not the reward function. The value of an action is it's overall utility; for example, an action may bring a high reward, but lead to low | Why (and when) does one have to learn the reward function from samples in reinforcement learning?
In reinforcement learning, the agent learns the value function, not the reward function. The value of an action is it's overall utility; for example, an action may bring a high reward, but lead to low-value states, making ... | Why (and when) does one have to learn the reward function from samples in reinforcement learning?
In reinforcement learning, the agent learns the value function, not the reward function. The value of an action is it's overall utility; for example, an action may bring a high reward, but lead to low |
36,650 | Why (and when) does one have to learn the reward function from samples in reinforcement learning? | I think those notes are slightly confusing. The rewards should be designed by you to encourage your RL agent to optimise the behaviour you want to see. The transitions between states are given by the enviroment and will often be estimated from data. I think what Andrew is refering to is a situation where from state A y... | Why (and when) does one have to learn the reward function from samples in reinforcement learning? | I think those notes are slightly confusing. The rewards should be designed by you to encourage your RL agent to optimise the behaviour you want to see. The transitions between states are given by the | Why (and when) does one have to learn the reward function from samples in reinforcement learning?
I think those notes are slightly confusing. The rewards should be designed by you to encourage your RL agent to optimise the behaviour you want to see. The transitions between states are given by the enviroment and will of... | Why (and when) does one have to learn the reward function from samples in reinforcement learning?
I think those notes are slightly confusing. The rewards should be designed by you to encourage your RL agent to optimise the behaviour you want to see. The transitions between states are given by the |
36,651 | Is there a consensus on adjusting alpha for multiple contrasts if the main effect is significant? | The idea that only non-orthogonal comparisons require adjustment is a myth. See section 6.1 of Frane (2015): http://jrp.icaap.org/index.php/jrp/article/view/514/417
In general, computing several alternate statistics and picking the one that gives you the answer you like best is a bad policy and can cause error inflatio... | Is there a consensus on adjusting alpha for multiple contrasts if the main effect is significant? | The idea that only non-orthogonal comparisons require adjustment is a myth. See section 6.1 of Frane (2015): http://jrp.icaap.org/index.php/jrp/article/view/514/417
In general, computing several alter | Is there a consensus on adjusting alpha for multiple contrasts if the main effect is significant?
The idea that only non-orthogonal comparisons require adjustment is a myth. See section 6.1 of Frane (2015): http://jrp.icaap.org/index.php/jrp/article/view/514/417
In general, computing several alternate statistics and pi... | Is there a consensus on adjusting alpha for multiple contrasts if the main effect is significant?
The idea that only non-orthogonal comparisons require adjustment is a myth. See section 6.1 of Frane (2015): http://jrp.icaap.org/index.php/jrp/article/view/514/417
In general, computing several alter |
36,652 | Is there a consensus on adjusting alpha for multiple contrasts if the main effect is significant? | For whatever it’s worth I offer the following comments:
I say there shouldn’t be or can’t be any consensus (or at least a
single way to do it) because there are multiple ways to characterize
such adjustments and the severity of consequences of any adjustment
for multiplicity depends on the subject matter, politics, ec... | Is there a consensus on adjusting alpha for multiple contrasts if the main effect is significant? | For whatever it’s worth I offer the following comments:
I say there shouldn’t be or can’t be any consensus (or at least a
single way to do it) because there are multiple ways to characterize
such adj | Is there a consensus on adjusting alpha for multiple contrasts if the main effect is significant?
For whatever it’s worth I offer the following comments:
I say there shouldn’t be or can’t be any consensus (or at least a
single way to do it) because there are multiple ways to characterize
such adjustments and the sever... | Is there a consensus on adjusting alpha for multiple contrasts if the main effect is significant?
For whatever it’s worth I offer the following comments:
I say there shouldn’t be or can’t be any consensus (or at least a
single way to do it) because there are multiple ways to characterize
such adj |
36,653 | Is there a consensus on adjusting alpha for multiple contrasts if the main effect is significant? | TL;DR
With respect to the example in your question, you mention three tests:
$H_0^{(1)}: \mu_1=\mu_2$ versus $H_1^{(1)}: \mu_1 \ne \mu_2$
$H_0^{(2)}: \mu_1=\mu_3$ versus $H_1^{(2)}: \mu_1 \ne \mu_3$
$H_0^{(3)}: \mu_1=\mu_4$ versus $H_1^{(3)}: \mu_1 \ne \mu_4$
Let us take one individual test, e.g. the first one. As ... | Is there a consensus on adjusting alpha for multiple contrasts if the main effect is significant? | TL;DR
With respect to the example in your question, you mention three tests:
$H_0^{(1)}: \mu_1=\mu_2$ versus $H_1^{(1)}: \mu_1 \ne \mu_2$
$H_0^{(2)}: \mu_1=\mu_3$ versus $H_1^{(2)}: \mu_1 \ne \mu_3$ | Is there a consensus on adjusting alpha for multiple contrasts if the main effect is significant?
TL;DR
With respect to the example in your question, you mention three tests:
$H_0^{(1)}: \mu_1=\mu_2$ versus $H_1^{(1)}: \mu_1 \ne \mu_2$
$H_0^{(2)}: \mu_1=\mu_3$ versus $H_1^{(2)}: \mu_1 \ne \mu_3$
$H_0^{(3)}: \mu_1=\mu... | Is there a consensus on adjusting alpha for multiple contrasts if the main effect is significant?
TL;DR
With respect to the example in your question, you mention three tests:
$H_0^{(1)}: \mu_1=\mu_2$ versus $H_1^{(1)}: \mu_1 \ne \mu_2$
$H_0^{(2)}: \mu_1=\mu_3$ versus $H_1^{(2)}: \mu_1 \ne \mu_3$ |
36,654 | What is the difference between buckets and bins? | A very good question, and a question that I myself had because I have heard these called buckets, groups, groupings, categories, categorical variables, discrete variables, and bins as I have changed disciplines. In general, use the language that the end-users of your analysis are most comfortable using - in a sense, sp... | What is the difference between buckets and bins? | A very good question, and a question that I myself had because I have heard these called buckets, groups, groupings, categories, categorical variables, discrete variables, and bins as I have changed d | What is the difference between buckets and bins?
A very good question, and a question that I myself had because I have heard these called buckets, groups, groupings, categories, categorical variables, discrete variables, and bins as I have changed disciplines. In general, use the language that the end-users of your ana... | What is the difference between buckets and bins?
A very good question, and a question that I myself had because I have heard these called buckets, groups, groupings, categories, categorical variables, discrete variables, and bins as I have changed d |
36,655 | Which econometric models can be used to forecast security returns + ARIMA/GARCH questions | My goal is simply to ... find statistically significant predictive results. Also, is there a particular market you would look at (energy, rates, equities)?
Most if not all the established and liquid financial markets will be very hard to predict whatever model you will use. If markets were relatively easy to predict, ... | Which econometric models can be used to forecast security returns + ARIMA/GARCH questions | My goal is simply to ... find statistically significant predictive results. Also, is there a particular market you would look at (energy, rates, equities)?
Most if not all the established and liquid | Which econometric models can be used to forecast security returns + ARIMA/GARCH questions
My goal is simply to ... find statistically significant predictive results. Also, is there a particular market you would look at (energy, rates, equities)?
Most if not all the established and liquid financial markets will be very... | Which econometric models can be used to forecast security returns + ARIMA/GARCH questions
My goal is simply to ... find statistically significant predictive results. Also, is there a particular market you would look at (energy, rates, equities)?
Most if not all the established and liquid |
36,656 | Which econometric models can be used to forecast security returns + ARIMA/GARCH questions | I applaud your enthusiasm for the subject. There is a lot of applications and methods to help with prediction but it is clear that there is no silver bullet. Just like there is no one weather model that predict all weather in all locations with equal accuracy, there is not model that can predict financial time series... | Which econometric models can be used to forecast security returns + ARIMA/GARCH questions | I applaud your enthusiasm for the subject. There is a lot of applications and methods to help with prediction but it is clear that there is no silver bullet. Just like there is no one weather model | Which econometric models can be used to forecast security returns + ARIMA/GARCH questions
I applaud your enthusiasm for the subject. There is a lot of applications and methods to help with prediction but it is clear that there is no silver bullet. Just like there is no one weather model that predict all weather in al... | Which econometric models can be used to forecast security returns + ARIMA/GARCH questions
I applaud your enthusiasm for the subject. There is a lot of applications and methods to help with prediction but it is clear that there is no silver bullet. Just like there is no one weather model |
36,657 | Effect of categorical interaction terms with random forest machine learning algorithm | I think your questions are very interesting, I spend some of my time looking at the effective mapping curvature of random forest(RF) model fits. RF can capture some orders of interactions depending on the situation. x1 * x2 is a two-way interaction and so on... You did not write how many levels your categorical predict... | Effect of categorical interaction terms with random forest machine learning algorithm | I think your questions are very interesting, I spend some of my time looking at the effective mapping curvature of random forest(RF) model fits. RF can capture some orders of interactions depending on | Effect of categorical interaction terms with random forest machine learning algorithm
I think your questions are very interesting, I spend some of my time looking at the effective mapping curvature of random forest(RF) model fits. RF can capture some orders of interactions depending on the situation. x1 * x2 is a two-w... | Effect of categorical interaction terms with random forest machine learning algorithm
I think your questions are very interesting, I spend some of my time looking at the effective mapping curvature of random forest(RF) model fits. RF can capture some orders of interactions depending on |
36,658 | Proper cross validation for stacking models | Question 1: local prediction & cross validation
Looking for closeby cases and upweighting them for prediction is referred to as local models or local prediction.
For the proper way to do cross validation, remember that for each fold, you only use training cases, and then do with the test cases exactly what you do for ... | Proper cross validation for stacking models | Question 1: local prediction & cross validation
Looking for closeby cases and upweighting them for prediction is referred to as local models or local prediction.
For the proper way to do cross valida | Proper cross validation for stacking models
Question 1: local prediction & cross validation
Looking for closeby cases and upweighting them for prediction is referred to as local models or local prediction.
For the proper way to do cross validation, remember that for each fold, you only use training cases, and then do ... | Proper cross validation for stacking models
Question 1: local prediction & cross validation
Looking for closeby cases and upweighting them for prediction is referred to as local models or local prediction.
For the proper way to do cross valida |
36,659 | Proper cross validation for stacking models | Question 1:
This is really similar to what is called KFold Target Encoding, and a correct way to do it is explained here:
https://medium.com/@pouryaayria/k-fold-target-encoding-dfe9a594874b
Your encoding is slightly different than what is describe in the article above, but you can apply the same design. | Proper cross validation for stacking models | Question 1:
This is really similar to what is called KFold Target Encoding, and a correct way to do it is explained here:
https://medium.com/@pouryaayria/k-fold-target-encoding-dfe9a594874b
Your encod | Proper cross validation for stacking models
Question 1:
This is really similar to what is called KFold Target Encoding, and a correct way to do it is explained here:
https://medium.com/@pouryaayria/k-fold-target-encoding-dfe9a594874b
Your encoding is slightly different than what is describe in the article above, but yo... | Proper cross validation for stacking models
Question 1:
This is really similar to what is called KFold Target Encoding, and a correct way to do it is explained here:
https://medium.com/@pouryaayria/k-fold-target-encoding-dfe9a594874b
Your encod |
36,660 | Does the uniform distribution have the greatest variance among all concave distributions on a bounded interval? | Yes, this is true.
First, note that any concave PDF must be unimodal. (This is an elementary consequence of the concavity.)
The answer to a related question shows that on the interval $[0,1]$, the variance of a distribution with mean $\mu$ cannot exceed $\mu(2-3\mu)/3$ (when $0 \le \mu \le 1/2$) or $(1-\mu)(3\mu-1)/3$... | Does the uniform distribution have the greatest variance among all concave distributions on a bounde | Yes, this is true.
First, note that any concave PDF must be unimodal. (This is an elementary consequence of the concavity.)
The answer to a related question shows that on the interval $[0,1]$, the va | Does the uniform distribution have the greatest variance among all concave distributions on a bounded interval?
Yes, this is true.
First, note that any concave PDF must be unimodal. (This is an elementary consequence of the concavity.)
The answer to a related question shows that on the interval $[0,1]$, the variance o... | Does the uniform distribution have the greatest variance among all concave distributions on a bounde
Yes, this is true.
First, note that any concave PDF must be unimodal. (This is an elementary consequence of the concavity.)
The answer to a related question shows that on the interval $[0,1]$, the va |
36,661 | Testing Whether a Binomial Distribution Fits Data | Steps in carrying out a chi-square goodness of fit for a binomial:
Compute an efficient estimate of $p$. The usual estimator will do nicely.
Calculate the probability of getting Type $i$ for each $i$, given that Type is drawn from a $\text{binomial}(n\hat{p})$.
Hence calculate the expected number of observations at ea... | Testing Whether a Binomial Distribution Fits Data | Steps in carrying out a chi-square goodness of fit for a binomial:
Compute an efficient estimate of $p$. The usual estimator will do nicely.
Calculate the probability of getting Type $i$ for each $i$ | Testing Whether a Binomial Distribution Fits Data
Steps in carrying out a chi-square goodness of fit for a binomial:
Compute an efficient estimate of $p$. The usual estimator will do nicely.
Calculate the probability of getting Type $i$ for each $i$, given that Type is drawn from a $\text{binomial}(n\hat{p})$.
Hence c... | Testing Whether a Binomial Distribution Fits Data
Steps in carrying out a chi-square goodness of fit for a binomial:
Compute an efficient estimate of $p$. The usual estimator will do nicely.
Calculate the probability of getting Type $i$ for each $i$ |
36,662 | Upper Bound on $E[\frac{1}{1-X}]$ where $E[X]=a$ and $0<a<1$ | There is no upper bound.
Intuitively, if $X$ has substantial support along a sequence approaching $1$, then $1/(1-X)$ could have a divergent (arbitrarily large) expectation. To show there is no upper bound, all we have to do is find a combination of support and probabilities that achieves the desired expectation of $a... | Upper Bound on $E[\frac{1}{1-X}]$ where $E[X]=a$ and $0<a<1$ | There is no upper bound.
Intuitively, if $X$ has substantial support along a sequence approaching $1$, then $1/(1-X)$ could have a divergent (arbitrarily large) expectation. To show there is no upper | Upper Bound on $E[\frac{1}{1-X}]$ where $E[X]=a$ and $0<a<1$
There is no upper bound.
Intuitively, if $X$ has substantial support along a sequence approaching $1$, then $1/(1-X)$ could have a divergent (arbitrarily large) expectation. To show there is no upper bound, all we have to do is find a combination of support ... | Upper Bound on $E[\frac{1}{1-X}]$ where $E[X]=a$ and $0<a<1$
There is no upper bound.
Intuitively, if $X$ has substantial support along a sequence approaching $1$, then $1/(1-X)$ could have a divergent (arbitrarily large) expectation. To show there is no upper |
36,663 | How is data generated in the Bayesian framework and what is the nature on the parameter that generates the data? | It is pretty straightforward: there are no differences between Bayesians and frequentists regarding the idea of the data-generating model.
To understand this, consider first that the data-generating model is mathematically encoded in the likelihood, which is the basis for the inference of Bayesians and frequentists al... | How is data generated in the Bayesian framework and what is the nature on the parameter that generat | It is pretty straightforward: there are no differences between Bayesians and frequentists regarding the idea of the data-generating model.
To understand this, consider first that the data-generating | How is data generated in the Bayesian framework and what is the nature on the parameter that generates the data?
It is pretty straightforward: there are no differences between Bayesians and frequentists regarding the idea of the data-generating model.
To understand this, consider first that the data-generating model i... | How is data generated in the Bayesian framework and what is the nature on the parameter that generat
It is pretty straightforward: there are no differences between Bayesians and frequentists regarding the idea of the data-generating model.
To understand this, consider first that the data-generating |
36,664 | How is data generated in the Bayesian framework and what is the nature on the parameter that generates the data? | Pages 3 and 4 of BDA by Gelman et al., 3rd ed., are illuminating. Bayesian statistics aims to make inference from data using probability
models for observables and unobservable quantities. We refer to the unobservable quantities as parameters, even if the distinction is not always clear-cut. In Bayesian statistics all ... | How is data generated in the Bayesian framework and what is the nature on the parameter that generat | Pages 3 and 4 of BDA by Gelman et al., 3rd ed., are illuminating. Bayesian statistics aims to make inference from data using probability
models for observables and unobservable quantities. We refer to | How is data generated in the Bayesian framework and what is the nature on the parameter that generates the data?
Pages 3 and 4 of BDA by Gelman et al., 3rd ed., are illuminating. Bayesian statistics aims to make inference from data using probability
models for observables and unobservable quantities. We refer to the un... | How is data generated in the Bayesian framework and what is the nature on the parameter that generat
Pages 3 and 4 of BDA by Gelman et al., 3rd ed., are illuminating. Bayesian statistics aims to make inference from data using probability
models for observables and unobservable quantities. We refer to |
36,665 | How is data generated in the Bayesian framework and what is the nature on the parameter that generates the data? | Is this the standard way to interpret the data generation process for in the Bayesian framework?
No, this is not the standard interpretation. In fact, you have already recognised in your question the "subjective" interpretation of probability, which is the standard basis of Bayesian statistics. Under the "subjectivi... | How is data generated in the Bayesian framework and what is the nature on the parameter that generat | Is this the standard way to interpret the data generation process for in the Bayesian framework?
No, this is not the standard interpretation. In fact, you have already recognised in your question th | How is data generated in the Bayesian framework and what is the nature on the parameter that generates the data?
Is this the standard way to interpret the data generation process for in the Bayesian framework?
No, this is not the standard interpretation. In fact, you have already recognised in your question the "subj... | How is data generated in the Bayesian framework and what is the nature on the parameter that generat
Is this the standard way to interpret the data generation process for in the Bayesian framework?
No, this is not the standard interpretation. In fact, you have already recognised in your question th |
36,666 | How is data generated in the Bayesian framework and what is the nature on the parameter that generates the data? | The parameter $\theta$ can only be regarded as fixed but unknown if you assume that the underlying model that you are working with is a perfect representation of the true system. However, since nature is usually much more complex than any mathematical model that we use, this assumption cannot be made. Therefore, there ... | How is data generated in the Bayesian framework and what is the nature on the parameter that generat | The parameter $\theta$ can only be regarded as fixed but unknown if you assume that the underlying model that you are working with is a perfect representation of the true system. However, since nature | How is data generated in the Bayesian framework and what is the nature on the parameter that generates the data?
The parameter $\theta$ can only be regarded as fixed but unknown if you assume that the underlying model that you are working with is a perfect representation of the true system. However, since nature is usu... | How is data generated in the Bayesian framework and what is the nature on the parameter that generat
The parameter $\theta$ can only be regarded as fixed but unknown if you assume that the underlying model that you are working with is a perfect representation of the true system. However, since nature |
36,667 | Outlier detection with data (which has categorical and numeric variables) with R | Let's look at a standard definition for outliers in fraud detection first (paraphrased from Han et al. Data Mining, 2012):
A customer generates transactions, which follow roughly a Gaussian distribution, consider e.g. buying a bigger lunch one day, a smaller the other and so on. An outlier is a data object, which devi... | Outlier detection with data (which has categorical and numeric variables) with R | Let's look at a standard definition for outliers in fraud detection first (paraphrased from Han et al. Data Mining, 2012):
A customer generates transactions, which follow roughly a Gaussian distribut | Outlier detection with data (which has categorical and numeric variables) with R
Let's look at a standard definition for outliers in fraud detection first (paraphrased from Han et al. Data Mining, 2012):
A customer generates transactions, which follow roughly a Gaussian distribution, consider e.g. buying a bigger lunc... | Outlier detection with data (which has categorical and numeric variables) with R
Let's look at a standard definition for outliers in fraud detection first (paraphrased from Han et al. Data Mining, 2012):
A customer generates transactions, which follow roughly a Gaussian distribut |
36,668 | characteristic functions and symmetry | Yes. When the characteristic function $\phi_X$ of a random variable $X$ is real-valued, that means
$$\overline{\phi_X}(t) = \phi_X(t)$$
for all $t$. But for any characteristic function
$$\phi_{-X}(t) = \phi_X(-t) = \overline{\phi_X}(t),$$
showing that $X$ and $-X$ have identical distributions (since the cf determines... | characteristic functions and symmetry | Yes. When the characteristic function $\phi_X$ of a random variable $X$ is real-valued, that means
$$\overline{\phi_X}(t) = \phi_X(t)$$
for all $t$. But for any characteristic function
$$\phi_{-X}(t | characteristic functions and symmetry
Yes. When the characteristic function $\phi_X$ of a random variable $X$ is real-valued, that means
$$\overline{\phi_X}(t) = \phi_X(t)$$
for all $t$. But for any characteristic function
$$\phi_{-X}(t) = \phi_X(-t) = \overline{\phi_X}(t),$$
showing that $X$ and $-X$ have identical ... | characteristic functions and symmetry
Yes. When the characteristic function $\phi_X$ of a random variable $X$ is real-valued, that means
$$\overline{\phi_X}(t) = \phi_X(t)$$
for all $t$. But for any characteristic function
$$\phi_{-X}(t |
36,669 | Is stratified meta-analysis more or less objective than meta-regression? | Here are some suggestions for how you could respond:
Given that your predictors are continuous (and artificially categorizing predictors is usually frowned upon), meta-regression seems like the right approach (and in fact, meta-regression can also deal with categorical predictors just as well as stratifying on them, s... | Is stratified meta-analysis more or less objective than meta-regression? | Here are some suggestions for how you could respond:
Given that your predictors are continuous (and artificially categorizing predictors is usually frowned upon), meta-regression seems like the right | Is stratified meta-analysis more or less objective than meta-regression?
Here are some suggestions for how you could respond:
Given that your predictors are continuous (and artificially categorizing predictors is usually frowned upon), meta-regression seems like the right approach (and in fact, meta-regression can als... | Is stratified meta-analysis more or less objective than meta-regression?
Here are some suggestions for how you could respond:
Given that your predictors are continuous (and artificially categorizing predictors is usually frowned upon), meta-regression seems like the right |
36,670 | Deep learning algorithm | Deep convex network is relatively new architecture of deep neural networks, which has been developed in order to overcome the scalability limitations of deep belief networks (DBN). You can read more on technical details of the architecture and its performance in research papers, for example, in this paper as well as a ... | Deep learning algorithm | Deep convex network is relatively new architecture of deep neural networks, which has been developed in order to overcome the scalability limitations of deep belief networks (DBN). You can read more o | Deep learning algorithm
Deep convex network is relatively new architecture of deep neural networks, which has been developed in order to overcome the scalability limitations of deep belief networks (DBN). You can read more on technical details of the architecture and its performance in research papers, for example, in ... | Deep learning algorithm
Deep convex network is relatively new architecture of deep neural networks, which has been developed in order to overcome the scalability limitations of deep belief networks (DBN). You can read more o |
36,671 | Analysing the residuals themselves | This has been referred to as residual index, although not consistently. I guess the type of analyses you subject it to would depend on your question of interest (as most result in some level of 'deeper understanding'), and so would pros and cons. Garcia-Berthou discusses cons of one example of such application as "an a... | Analysing the residuals themselves | This has been referred to as residual index, although not consistently. I guess the type of analyses you subject it to would depend on your question of interest (as most result in some level of 'deepe | Analysing the residuals themselves
This has been referred to as residual index, although not consistently. I guess the type of analyses you subject it to would depend on your question of interest (as most result in some level of 'deeper understanding'), and so would pros and cons. Garcia-Berthou discusses cons of one e... | Analysing the residuals themselves
This has been referred to as residual index, although not consistently. I guess the type of analyses you subject it to would depend on your question of interest (as most result in some level of 'deepe |
36,672 | Analysing the residuals themselves | As a partial answer to your question, you can build a residual model (also known as a variance model) to
model the residuals for the original model as a function of the predicted response (for example) as follows (in R notation):
residual.model <-
lm(abs(residuals(original.model)) ~ predict(original.model), ...)
... | Analysing the residuals themselves | As a partial answer to your question, you can build a residual model (also known as a variance model) to
model the residuals for the original model as a function of the predicted response (for exampl | Analysing the residuals themselves
As a partial answer to your question, you can build a residual model (also known as a variance model) to
model the residuals for the original model as a function of the predicted response (for example) as follows (in R notation):
residual.model <-
lm(abs(residuals(original.model... | Analysing the residuals themselves
As a partial answer to your question, you can build a residual model (also known as a variance model) to
model the residuals for the original model as a function of the predicted response (for exampl |
36,673 | What's the difference between estimating equations and method of moments estimators? | The most common justification of the method of moments is simply the law of large numbers, which would seem to make your suggestion of estimating $\mu_3$ by $\hat{\mu}_3$ "method of moments" (and I'd be inclined to call it MoM in any case).
However, a number of books and documents, such as this for example (and to some... | What's the difference between estimating equations and method of moments estimators? | The most common justification of the method of moments is simply the law of large numbers, which would seem to make your suggestion of estimating $\mu_3$ by $\hat{\mu}_3$ "method of moments" (and I'd | What's the difference between estimating equations and method of moments estimators?
The most common justification of the method of moments is simply the law of large numbers, which would seem to make your suggestion of estimating $\mu_3$ by $\hat{\mu}_3$ "method of moments" (and I'd be inclined to call it MoM in any c... | What's the difference between estimating equations and method of moments estimators?
The most common justification of the method of moments is simply the law of large numbers, which would seem to make your suggestion of estimating $\mu_3$ by $\hat{\mu}_3$ "method of moments" (and I'd |
36,674 | What's the difference between estimating equations and method of moments estimators? | Estimating equations is a more general method, it doesnt specify from where you get the estimating equation. Maximum likelihood is also an example of estimating equations, as it leads to the score equation. Various forms of quasi (or pseudo)-likelihood are other examples! as are method of moments. | What's the difference between estimating equations and method of moments estimators? | Estimating equations is a more general method, it doesnt specify from where you get the estimating equation. Maximum likelihood is also an example of estimating equations, as it leads to the score equ | What's the difference between estimating equations and method of moments estimators?
Estimating equations is a more general method, it doesnt specify from where you get the estimating equation. Maximum likelihood is also an example of estimating equations, as it leads to the score equation. Various forms of quasi (or ... | What's the difference between estimating equations and method of moments estimators?
Estimating equations is a more general method, it doesnt specify from where you get the estimating equation. Maximum likelihood is also an example of estimating equations, as it leads to the score equ |
36,675 | When does an unpaired test result in higher p-value than a paired test? | As @whuber says in the comment above, when the measures are negatively correlated, the p-value can be lower in the unpaired test than in the paired test. Here's an example where there is a difference:
library(MASS)
s <- matrix(c(1, -0.8, -0.8, 1), 2)
df <- mvrnorm(n=100, mu=c(0, 0.3), Sigma=s, empirical=TRUE)
t.test(... | When does an unpaired test result in higher p-value than a paired test? | As @whuber says in the comment above, when the measures are negatively correlated, the p-value can be lower in the unpaired test than in the paired test. Here's an example where there is a difference: | When does an unpaired test result in higher p-value than a paired test?
As @whuber says in the comment above, when the measures are negatively correlated, the p-value can be lower in the unpaired test than in the paired test. Here's an example where there is a difference:
library(MASS)
s <- matrix(c(1, -0.8, -0.8, 1)... | When does an unpaired test result in higher p-value than a paired test?
As @whuber says in the comment above, when the measures are negatively correlated, the p-value can be lower in the unpaired test than in the paired test. Here's an example where there is a difference: |
36,676 | Which post-hoc is more valid for multiple comparison of an unbalanced lmer-model: lsm or mcp? | Are you sure that the results really differ at all? I see only one change in the 4th decimal place (of the p-value of the difference between groups 2 and 3), a relative difference of 0.3%, which could be a numerical difference due to doing equivalent computations in a different sequence.
?lsm says:
It works similarl... | Which post-hoc is more valid for multiple comparison of an unbalanced lmer-model: lsm or mcp? | Are you sure that the results really differ at all? I see only one change in the 4th decimal place (of the p-value of the difference between groups 2 and 3), a relative difference of 0.3%, which coul | Which post-hoc is more valid for multiple comparison of an unbalanced lmer-model: lsm or mcp?
Are you sure that the results really differ at all? I see only one change in the 4th decimal place (of the p-value of the difference between groups 2 and 3), a relative difference of 0.3%, which could be a numerical differenc... | Which post-hoc is more valid for multiple comparison of an unbalanced lmer-model: lsm or mcp?
Are you sure that the results really differ at all? I see only one change in the 4th decimal place (of the p-value of the difference between groups 2 and 3), a relative difference of 0.3%, which coul |
36,677 | Cross correlation for very sparse binary data | You are right to think of correlation as the mean of the product of the standardized variables: this has great conceptual advantages over other definitions. It also leads to minimal loss of floating point precision. However, it is not necessarily the best approach for all computation, especially when speed is importan... | Cross correlation for very sparse binary data | You are right to think of correlation as the mean of the product of the standardized variables: this has great conceptual advantages over other definitions. It also leads to minimal loss of floating | Cross correlation for very sparse binary data
You are right to think of correlation as the mean of the product of the standardized variables: this has great conceptual advantages over other definitions. It also leads to minimal loss of floating point precision. However, it is not necessarily the best approach for all ... | Cross correlation for very sparse binary data
You are right to think of correlation as the mean of the product of the standardized variables: this has great conceptual advantages over other definitions. It also leads to minimal loss of floating |
36,678 | Using proper scoring rule to determine class membership from logistic regression | What you need to do is "put the model into production", that is, as you say, to actually predict which cases will be "yes", what you need is a loss function. You have two possible errors, saying "yes" when no, saying "no" when yes. Do these errors have economical consequences, costs associated? Do these costs depend o... | Using proper scoring rule to determine class membership from logistic regression | What you need to do is "put the model into production", that is, as you say, to actually predict which cases will be "yes", what you need is a loss function. You have two possible errors, saying "yes | Using proper scoring rule to determine class membership from logistic regression
What you need to do is "put the model into production", that is, as you say, to actually predict which cases will be "yes", what you need is a loss function. You have two possible errors, saying "yes" when no, saying "no" when yes. Do the... | Using proper scoring rule to determine class membership from logistic regression
What you need to do is "put the model into production", that is, as you say, to actually predict which cases will be "yes", what you need is a loss function. You have two possible errors, saying "yes |
36,679 | What is the difference between ex ante and a priori, if any? | Ex Ante means before the event. Ex Post means after the event. In this example, I think this means before and after the event that gives the statistical difference you're testing, respectively.
On the other hand, a priori and a posteriori are terms from philosophy, respectively denoting knowledge that is logically deri... | What is the difference between ex ante and a priori, if any? | Ex Ante means before the event. Ex Post means after the event. In this example, I think this means before and after the event that gives the statistical difference you're testing, respectively.
On the | What is the difference between ex ante and a priori, if any?
Ex Ante means before the event. Ex Post means after the event. In this example, I think this means before and after the event that gives the statistical difference you're testing, respectively.
On the other hand, a priori and a posteriori are terms from philo... | What is the difference between ex ante and a priori, if any?
Ex Ante means before the event. Ex Post means after the event. In this example, I think this means before and after the event that gives the statistical difference you're testing, respectively.
On the |
36,680 | What is the difference between ex ante and a priori, if any? | "Ex ante" & "ex post" are used in forecasting:
An ex ante forecast uses only the information available at a given time to predict what'll happen after that time.
An ex post forecast also uses information available after the given time.
So while an ex post sales forecast based on GDP growth in a region may be informativ... | What is the difference between ex ante and a priori, if any? | "Ex ante" & "ex post" are used in forecasting:
An ex ante forecast uses only the information available at a given time to predict what'll happen after that time.
An ex post forecast also uses informat | What is the difference between ex ante and a priori, if any?
"Ex ante" & "ex post" are used in forecasting:
An ex ante forecast uses only the information available at a given time to predict what'll happen after that time.
An ex post forecast also uses information available after the given time.
So while an ex post sal... | What is the difference between ex ante and a priori, if any?
"Ex ante" & "ex post" are used in forecasting:
An ex ante forecast uses only the information available at a given time to predict what'll happen after that time.
An ex post forecast also uses informat |
36,681 | What is the difference between ex ante and a priori, if any? | To me, there is a meaning of process in the term a priori: a priori, I think so, but my mind might change through the process of understanding of the new ideas. However, ex ante refers to a moment that is right before an event: ex ante Wall Street fell down, I sold my assets. | What is the difference between ex ante and a priori, if any? | To me, there is a meaning of process in the term a priori: a priori, I think so, but my mind might change through the process of understanding of the new ideas. However, ex ante refers to a moment tha | What is the difference between ex ante and a priori, if any?
To me, there is a meaning of process in the term a priori: a priori, I think so, but my mind might change through the process of understanding of the new ideas. However, ex ante refers to a moment that is right before an event: ex ante Wall Street fell down, ... | What is the difference between ex ante and a priori, if any?
To me, there is a meaning of process in the term a priori: a priori, I think so, but my mind might change through the process of understanding of the new ideas. However, ex ante refers to a moment tha |
36,682 | Prediction of continuous variable using "bnlearn" package in R | Why are you using table to compare the output? Using cbind to put the actual and predicted values side by side shows that the predictions are not the same as the actual, and you can compute standard accuracy metrics to quantify the degree to which they diverge.
library(bnlearn) # Load the package ... | Prediction of continuous variable using "bnlearn" package in R | Why are you using table to compare the output? Using cbind to put the actual and predicted values side by side shows that the predictions are not the same as the actual, and you can compute standard a | Prediction of continuous variable using "bnlearn" package in R
Why are you using table to compare the output? Using cbind to put the actual and predicted values side by side shows that the predictions are not the same as the actual, and you can compute standard accuracy metrics to quantify the degree to which they dive... | Prediction of continuous variable using "bnlearn" package in R
Why are you using table to compare the output? Using cbind to put the actual and predicted values side by side shows that the predictions are not the same as the actual, and you can compute standard a |
36,683 | Prediction of continuous variable using "bnlearn" package in R | For both the predicted sets you proposed (with both the Original values and zeros) I found the same output in R.
[1] 3.5749952 0.7434548 5.1731669 10.0840800 12.3966908 9.1834888 6.8067145
[8] 9.9246630 5.9426798 16.0056136
This shows that the values of C are irrelevant. Furthermore, test.set$c provides you w... | Prediction of continuous variable using "bnlearn" package in R | For both the predicted sets you proposed (with both the Original values and zeros) I found the same output in R.
[1] 3.5749952 0.7434548 5.1731669 10.0840800 12.3966908 9.1834888 6.8067145
[8] | Prediction of continuous variable using "bnlearn" package in R
For both the predicted sets you proposed (with both the Original values and zeros) I found the same output in R.
[1] 3.5749952 0.7434548 5.1731669 10.0840800 12.3966908 9.1834888 6.8067145
[8] 9.9246630 5.9426798 16.0056136
This shows that the val... | Prediction of continuous variable using "bnlearn" package in R
For both the predicted sets you proposed (with both the Original values and zeros) I found the same output in R.
[1] 3.5749952 0.7434548 5.1731669 10.0840800 12.3966908 9.1834888 6.8067145
[8] |
36,684 | Prediction of continuous variable using "bnlearn" package in R | The equivalent for the discrete case occurs (inability to set the target variable to zero).
In this case do the following:
test.set\$TARGET<-as.factor(0)
levels(test.set\$TARGET) <- c(level1,level2,level3...) | Prediction of continuous variable using "bnlearn" package in R | The equivalent for the discrete case occurs (inability to set the target variable to zero).
In this case do the following:
test.set\$TARGET<-as.factor(0)
levels(test.set\$TARGET) <- c(level1,level2, | Prediction of continuous variable using "bnlearn" package in R
The equivalent for the discrete case occurs (inability to set the target variable to zero).
In this case do the following:
test.set\$TARGET<-as.factor(0)
levels(test.set\$TARGET) <- c(level1,level2,level3...) | Prediction of continuous variable using "bnlearn" package in R
The equivalent for the discrete case occurs (inability to set the target variable to zero).
In this case do the following:
test.set\$TARGET<-as.factor(0)
levels(test.set\$TARGET) <- c(level1,level2, |
36,685 | Covariance term in simple linear regression | I was trying to prove the same
and struggled a bit but finally got to a solution. I know this was asked long back but there might be other people interested.
$$Cov (\bar{Y}, \hat{\beta_1}) = E[(\bar{Y} - E(\bar{Y}))(\hat{\beta}_1 - \beta_1)]\\
= E[(\beta_0 + \bar{X}\beta_1 + \bar{\epsilon} - \beta_0 - \bar{X} \beta_1)... | Covariance term in simple linear regression | I was trying to prove the same
and struggled a bit but finally got to a solution. I know this was asked long back but there might be other people interested.
$$Cov (\bar{Y}, \hat{\beta_1}) = E[(\bar{ | Covariance term in simple linear regression
I was trying to prove the same
and struggled a bit but finally got to a solution. I know this was asked long back but there might be other people interested.
$$Cov (\bar{Y}, \hat{\beta_1}) = E[(\bar{Y} - E(\bar{Y}))(\hat{\beta}_1 - \beta_1)]\\
= E[(\beta_0 + \bar{X}\beta_1 +... | Covariance term in simple linear regression
I was trying to prove the same
and struggled a bit but finally got to a solution. I know this was asked long back but there might be other people interested.
$$Cov (\bar{Y}, \hat{\beta_1}) = E[(\bar{ |
36,686 | Covariance term in simple linear regression | $$
{\rm Cov}\bigg[\frac 1 n \sum y_i,\ \frac{\sum(x_i - \bar x)y_i}{\sum(x_i-\bar x)^2}\bigg] = \frac 1 n \times \frac{\sum(x_i-\bar x)\sigma^2}{\sum(x_i - \bar x)^2} = 1$$ it does not equal zero this way I believe | Covariance term in simple linear regression | $$
{\rm Cov}\bigg[\frac 1 n \sum y_i,\ \frac{\sum(x_i - \bar x)y_i}{\sum(x_i-\bar x)^2}\bigg] = \frac 1 n \times \frac{\sum(x_i-\bar x)\sigma^2}{\sum(x_i - \bar x)^2} = 1$$ it does not equal zero this | Covariance term in simple linear regression
$$
{\rm Cov}\bigg[\frac 1 n \sum y_i,\ \frac{\sum(x_i - \bar x)y_i}{\sum(x_i-\bar x)^2}\bigg] = \frac 1 n \times \frac{\sum(x_i-\bar x)\sigma^2}{\sum(x_i - \bar x)^2} = 1$$ it does not equal zero this way I believe | Covariance term in simple linear regression
$$
{\rm Cov}\bigg[\frac 1 n \sum y_i,\ \frac{\sum(x_i - \bar x)y_i}{\sum(x_i-\bar x)^2}\bigg] = \frac 1 n \times \frac{\sum(x_i-\bar x)\sigma^2}{\sum(x_i - \bar x)^2} = 1$$ it does not equal zero this |
36,687 | How to gently introduce epidemiologists/public health coworkers to advanced predictive modeling? | I'm going to weigh in as an Epidemiologist.
I can see inertia setting in as researchers & professionals in the health care field move into middle management and beyond and are out of touch with new developments in statistics.
First, I would strongly advise you not to assume this is simply inertia, either in the form... | How to gently introduce epidemiologists/public health coworkers to advanced predictive modeling? | I'm going to weigh in as an Epidemiologist.
I can see inertia setting in as researchers & professionals in the health care field move into middle management and beyond and are out of touch with new d | How to gently introduce epidemiologists/public health coworkers to advanced predictive modeling?
I'm going to weigh in as an Epidemiologist.
I can see inertia setting in as researchers & professionals in the health care field move into middle management and beyond and are out of touch with new developments in statisti... | How to gently introduce epidemiologists/public health coworkers to advanced predictive modeling?
I'm going to weigh in as an Epidemiologist.
I can see inertia setting in as researchers & professionals in the health care field move into middle management and beyond and are out of touch with new d |
36,688 | Polynomial term in logistic regression | Logistic regression models the log odds of a "1" or "success" response as a linear function of the regression coefficients (i.e. the parameters), but there's no need to insist that the log odds be a linear function of predictors.
[This model is linear in parameters & the predictor:
$$\operatorname{logit}\pi_i = \beta_0... | Polynomial term in logistic regression | Logistic regression models the log odds of a "1" or "success" response as a linear function of the regression coefficients (i.e. the parameters), but there's no need to insist that the log odds be a l | Polynomial term in logistic regression
Logistic regression models the log odds of a "1" or "success" response as a linear function of the regression coefficients (i.e. the parameters), but there's no need to insist that the log odds be a linear function of predictors.
[This model is linear in parameters & the predictor... | Polynomial term in logistic regression
Logistic regression models the log odds of a "1" or "success" response as a linear function of the regression coefficients (i.e. the parameters), but there's no need to insist that the log odds be a l |
36,689 | Advice on sensitivity analysis for priors in Bayesian statistics | Try as I might, I'm unable to find an open source that fully explains that process, so for a fuller treatment than the below, I'm left to direct your to Bayesian Data Analysis. In particular, Ch. 6 should prove very helpful.
In broadest strokes, following the analysis one should ponder whether model inferences make rea... | Advice on sensitivity analysis for priors in Bayesian statistics | Try as I might, I'm unable to find an open source that fully explains that process, so for a fuller treatment than the below, I'm left to direct your to Bayesian Data Analysis. In particular, Ch. 6 sh | Advice on sensitivity analysis for priors in Bayesian statistics
Try as I might, I'm unable to find an open source that fully explains that process, so for a fuller treatment than the below, I'm left to direct your to Bayesian Data Analysis. In particular, Ch. 6 should prove very helpful.
In broadest strokes, following... | Advice on sensitivity analysis for priors in Bayesian statistics
Try as I might, I'm unable to find an open source that fully explains that process, so for a fuller treatment than the below, I'm left to direct your to Bayesian Data Analysis. In particular, Ch. 6 sh |
36,690 | Linearity Assumption in OLS with Dummy Variables | It's time this question had an answer.
I have a continuous response variable and a regression model with multiple predictors.
Most of my predictors are continuous
The continuity or otherwise of a predictor makes no no difference; a relationship only need be linear over the domain where it's defined, for it to be l... | Linearity Assumption in OLS with Dummy Variables | It's time this question had an answer.
I have a continuous response variable and a regression model with multiple predictors.
Most of my predictors are continuous
The continuity or otherwise of a | Linearity Assumption in OLS with Dummy Variables
It's time this question had an answer.
I have a continuous response variable and a regression model with multiple predictors.
Most of my predictors are continuous
The continuity or otherwise of a predictor makes no no difference; a relationship only need be linear o... | Linearity Assumption in OLS with Dummy Variables
It's time this question had an answer.
I have a continuous response variable and a regression model with multiple predictors.
Most of my predictors are continuous
The continuity or otherwise of a |
36,691 | Linearity Assumption in OLS with Dummy Variables | Adding to the excellent answer by Glen_b, it is worth noting that adding a categorical variable into a regression cannot cause problems with the linearity assumption in and of itself. If there is an issue of non-linearity in the model it will be due to some underlying relationship that is not captured by the specified... | Linearity Assumption in OLS with Dummy Variables | Adding to the excellent answer by Glen_b, it is worth noting that adding a categorical variable into a regression cannot cause problems with the linearity assumption in and of itself. If there is an | Linearity Assumption in OLS with Dummy Variables
Adding to the excellent answer by Glen_b, it is worth noting that adding a categorical variable into a regression cannot cause problems with the linearity assumption in and of itself. If there is an issue of non-linearity in the model it will be due to some underlying r... | Linearity Assumption in OLS with Dummy Variables
Adding to the excellent answer by Glen_b, it is worth noting that adding a categorical variable into a regression cannot cause problems with the linearity assumption in and of itself. If there is an |
36,692 | How to derive the $\alpha$ for the Pareto rule | The basic result is:
$\alpha=\log_4 5=1.160964...$
The calculation comes from the Lorenz curve; specifically you're asking for the $\alpha$ for which $L(0.8)=0.2$.
$L$ is defined as
$$L(F)=\frac{\int_{x_\mathrm{m}}^{x(F)}xf(x)\,dx}{\int_{x_\mathrm{m}}^\infty xf(x)\,dx} =\frac{\int_0^F x(F')\,dF'}{\int_0^1 x(F')\,dF'}$$... | How to derive the $\alpha$ for the Pareto rule | The basic result is:
$\alpha=\log_4 5=1.160964...$
The calculation comes from the Lorenz curve; specifically you're asking for the $\alpha$ for which $L(0.8)=0.2$.
$L$ is defined as
$$L(F)=\frac{\int_ | How to derive the $\alpha$ for the Pareto rule
The basic result is:
$\alpha=\log_4 5=1.160964...$
The calculation comes from the Lorenz curve; specifically you're asking for the $\alpha$ for which $L(0.8)=0.2$.
$L$ is defined as
$$L(F)=\frac{\int_{x_\mathrm{m}}^{x(F)}xf(x)\,dx}{\int_{x_\mathrm{m}}^\infty xf(x)\,dx} =\f... | How to derive the $\alpha$ for the Pareto rule
The basic result is:
$\alpha=\log_4 5=1.160964...$
The calculation comes from the Lorenz curve; specifically you're asking for the $\alpha$ for which $L(0.8)=0.2$.
$L$ is defined as
$$L(F)=\frac{\int_ |
36,693 | Back Transformation | Your model and its estimates posit that
$$\sqrt{Y} = 2.1014 D - 3.0147 + \varepsilon$$
where $D$ is Dose.Back (or its logarithm) and $\varepsilon$ is a random variable of zero expectation whose standard deviation is approximately $16.28.$ Squaring both sides gives
$$Y = (2.1014 D - 3.0147 + \varepsilon)^2.$$
Adding... | Back Transformation | Your model and its estimates posit that
$$\sqrt{Y} = 2.1014 D - 3.0147 + \varepsilon$$
where $D$ is Dose.Back (or its logarithm) and $\varepsilon$ is a random variable of zero expectation whose stan | Back Transformation
Your model and its estimates posit that
$$\sqrt{Y} = 2.1014 D - 3.0147 + \varepsilon$$
where $D$ is Dose.Back (or its logarithm) and $\varepsilon$ is a random variable of zero expectation whose standard deviation is approximately $16.28.$ Squaring both sides gives
$$Y = (2.1014 D - 3.0147 + \var... | Back Transformation
Your model and its estimates posit that
$$\sqrt{Y} = 2.1014 D - 3.0147 + \varepsilon$$
where $D$ is Dose.Back (or its logarithm) and $\varepsilon$ is a random variable of zero expectation whose stan |
36,694 | Optimization of the regularized least squares with gradient descent | Instead of using a constant learning rate, set your learning rate to a reasonably large value (try $\alpha=1$) and then use "backoff" to shrink your learning rate as appropriate using the following algorithm:
while $f(w^{(k)} - \alpha d) \gt f(w^{(k)}) $: $\alpha := \alpha/2$
$w^{(k+1)} := w^{(k)} - \alpha d$
where $f$... | Optimization of the regularized least squares with gradient descent | Instead of using a constant learning rate, set your learning rate to a reasonably large value (try $\alpha=1$) and then use "backoff" to shrink your learning rate as appropriate using the following al | Optimization of the regularized least squares with gradient descent
Instead of using a constant learning rate, set your learning rate to a reasonably large value (try $\alpha=1$) and then use "backoff" to shrink your learning rate as appropriate using the following algorithm:
while $f(w^{(k)} - \alpha d) \gt f(w^{(k)})... | Optimization of the regularized least squares with gradient descent
Instead of using a constant learning rate, set your learning rate to a reasonably large value (try $\alpha=1$) and then use "backoff" to shrink your learning rate as appropriate using the following al |
36,695 | Optimization of the regularized least squares with gradient descent | As whuber commented, there is no need to bother with optimization algorithms. Gradient in matrix notation is
$$
2X'(Xw-y)+2\lambda w
$$
Equating it to zero in Matlab is a one-liner:
w_star = (X'*X+lambda*eye(d)) \ X'*y; | Optimization of the regularized least squares with gradient descent | As whuber commented, there is no need to bother with optimization algorithms. Gradient in matrix notation is
$$
2X'(Xw-y)+2\lambda w
$$
Equating it to zero in Matlab is a one-liner:
w_star = (X'*X+lam | Optimization of the regularized least squares with gradient descent
As whuber commented, there is no need to bother with optimization algorithms. Gradient in matrix notation is
$$
2X'(Xw-y)+2\lambda w
$$
Equating it to zero in Matlab is a one-liner:
w_star = (X'*X+lambda*eye(d)) \ X'*y; | Optimization of the regularized least squares with gradient descent
As whuber commented, there is no need to bother with optimization algorithms. Gradient in matrix notation is
$$
2X'(Xw-y)+2\lambda w
$$
Equating it to zero in Matlab is a one-liner:
w_star = (X'*X+lam |
36,696 | Errors in optim when fitting arima model in R | Edited: if you down-vote this can you please explain why? I'm new here.
I'm had the same problem. I looked around online and found a solution suggested elsewhere on Cross Validated. I figured I'd share here in case anyone wanted it.
I just added a "method="CSS"" to my model and it worked. For example:
model = Arima... | Errors in optim when fitting arima model in R | Edited: if you down-vote this can you please explain why? I'm new here.
I'm had the same problem. I looked around online and found a solution suggested elsewhere on Cross Validated. I figured I'd sha | Errors in optim when fitting arima model in R
Edited: if you down-vote this can you please explain why? I'm new here.
I'm had the same problem. I looked around online and found a solution suggested elsewhere on Cross Validated. I figured I'd share here in case anyone wanted it.
I just added a "method="CSS"" to my mod... | Errors in optim when fitting arima model in R
Edited: if you down-vote this can you please explain why? I'm new here.
I'm had the same problem. I looked around online and found a solution suggested elsewhere on Cross Validated. I figured I'd sha |
36,697 | Errors in optim when fitting arima model in R | Fitting the ARIMA model with Maximum Likelihood (method = "ML") requires optimising (minimising) the ARIMA model negative log-likelihood over the parameters. This turns out to be a constrained optimisation problem as the parameters must result in a stationary model. This nonlinear constraint is accounted for with the n... | Errors in optim when fitting arima model in R | Fitting the ARIMA model with Maximum Likelihood (method = "ML") requires optimising (minimising) the ARIMA model negative log-likelihood over the parameters. This turns out to be a constrained optimis | Errors in optim when fitting arima model in R
Fitting the ARIMA model with Maximum Likelihood (method = "ML") requires optimising (minimising) the ARIMA model negative log-likelihood over the parameters. This turns out to be a constrained optimisation problem as the parameters must result in a stationary model. This no... | Errors in optim when fitting arima model in R
Fitting the ARIMA model with Maximum Likelihood (method = "ML") requires optimising (minimising) the ARIMA model negative log-likelihood over the parameters. This turns out to be a constrained optimis |
36,698 | Errors in optim when fitting arima model in R | You seem to have problem with algorithms convergence. This happens sometimes with numerical optimization.
Here is link to wikipedia article on this particular optimization method:
http://en.wikipedia.org/wiki/Broyden%E2%80%93Fletcher%E2%80%93Goldfarb%E2%80%93Shanno_algorithm | Errors in optim when fitting arima model in R | You seem to have problem with algorithms convergence. This happens sometimes with numerical optimization.
Here is link to wikipedia article on this particular optimization method:
http://en.wikipedia. | Errors in optim when fitting arima model in R
You seem to have problem with algorithms convergence. This happens sometimes with numerical optimization.
Here is link to wikipedia article on this particular optimization method:
http://en.wikipedia.org/wiki/Broyden%E2%80%93Fletcher%E2%80%93Goldfarb%E2%80%93Shanno_algorith... | Errors in optim when fitting arima model in R
You seem to have problem with algorithms convergence. This happens sometimes with numerical optimization.
Here is link to wikipedia article on this particular optimization method:
http://en.wikipedia. |
36,699 | QR factorization and linear regression | First, rewriting the $QR$ decomposition in a (hopefully) clearer way by showing the partitioning of $Q$ and explicitly giving the dimensions of the $0$ matrix:
$$
X=QR=[Q_f,Q_g]\left[\begin{array}{c}R_1\\0_{(n-p)\times p}\end{array}\right]=Q_fR_1
$$
So I think part of the confusion here is that in the book you are refe... | QR factorization and linear regression | First, rewriting the $QR$ decomposition in a (hopefully) clearer way by showing the partitioning of $Q$ and explicitly giving the dimensions of the $0$ matrix:
$$
X=QR=[Q_f,Q_g]\left[\begin{array}{c}R | QR factorization and linear regression
First, rewriting the $QR$ decomposition in a (hopefully) clearer way by showing the partitioning of $Q$ and explicitly giving the dimensions of the $0$ matrix:
$$
X=QR=[Q_f,Q_g]\left[\begin{array}{c}R_1\\0_{(n-p)\times p}\end{array}\right]=Q_fR_1
$$
So I think part of the confusio... | QR factorization and linear regression
First, rewriting the $QR$ decomposition in a (hopefully) clearer way by showing the partitioning of $Q$ and explicitly giving the dimensions of the $0$ matrix:
$$
X=QR=[Q_f,Q_g]\left[\begin{array}{c}R |
36,700 | p-values different for binomial test vs. proportions test. Which to report? | If you are testing whether 44 occurrences out of 63 can be a sample from the binomial population with probability 80% of a success in individual independent trial, then the first test is certainly correct. | p-values different for binomial test vs. proportions test. Which to report? | If you are testing whether 44 occurrences out of 63 can be a sample from the binomial population with probability 80% of a success in individual independent trial, then the first test is certainly cor | p-values different for binomial test vs. proportions test. Which to report?
If you are testing whether 44 occurrences out of 63 can be a sample from the binomial population with probability 80% of a success in individual independent trial, then the first test is certainly correct. | p-values different for binomial test vs. proportions test. Which to report?
If you are testing whether 44 occurrences out of 63 can be a sample from the binomial population with probability 80% of a success in individual independent trial, then the first test is certainly cor |
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