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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37,401 | Is it possible to have an estimator that is unbiased and bounded? | I will present conditions under which an unbiased estimator remains unbiased, even after it is bounded. But I am not sure that they amount to something interesting or useful.
Let an estimator $\hat \theta$ of the unknown parameter $\theta$ of a continuous distribution, and $E(\hat \theta) =\theta$.
Assume that for so... | Is it possible to have an estimator that is unbiased and bounded? | I will present conditions under which an unbiased estimator remains unbiased, even after it is bounded. But I am not sure that they amount to something interesting or useful.
Let an estimator $\hat | Is it possible to have an estimator that is unbiased and bounded?
I will present conditions under which an unbiased estimator remains unbiased, even after it is bounded. But I am not sure that they amount to something interesting or useful.
Let an estimator $\hat \theta$ of the unknown parameter $\theta$ of a continu... | Is it possible to have an estimator that is unbiased and bounded?
I will present conditions under which an unbiased estimator remains unbiased, even after it is bounded. But I am not sure that they amount to something interesting or useful.
Let an estimator $\hat |
37,402 | Dickey-Fuller augmented tests: how to choose lags? | Use ac and pac in Stata to assess the possible lags. However, if you are using the ARMA model, it is normal to estimate arma for the candidate models with p=0, q=1 and so on to p=3 and q=3. Then obtain the aic and bic. The model with the lowest aic or bic is chosen. The lags chosen by these criteria may differ, but you... | Dickey-Fuller augmented tests: how to choose lags? | Use ac and pac in Stata to assess the possible lags. However, if you are using the ARMA model, it is normal to estimate arma for the candidate models with p=0, q=1 and so on to p=3 and q=3. Then obtai | Dickey-Fuller augmented tests: how to choose lags?
Use ac and pac in Stata to assess the possible lags. However, if you are using the ARMA model, it is normal to estimate arma for the candidate models with p=0, q=1 and so on to p=3 and q=3. Then obtain the aic and bic. The model with the lowest aic or bic is chosen. Th... | Dickey-Fuller augmented tests: how to choose lags?
Use ac and pac in Stata to assess the possible lags. However, if you are using the ARMA model, it is normal to estimate arma for the candidate models with p=0, q=1 and so on to p=3 and q=3. Then obtai |
37,403 | Bootstrapping power estimates for a bootstrap test | I don't think use bootstrap to artificially increase your sample size would be a good idea. Any violation of the assumption of the independence of the observations dramatically increase the odds od a spurious result (which would be the case when n1 is significantly greater than n0).
I would estimate the confidence int... | Bootstrapping power estimates for a bootstrap test | I don't think use bootstrap to artificially increase your sample size would be a good idea. Any violation of the assumption of the independence of the observations dramatically increase the odds od a | Bootstrapping power estimates for a bootstrap test
I don't think use bootstrap to artificially increase your sample size would be a good idea. Any violation of the assumption of the independence of the observations dramatically increase the odds od a spurious result (which would be the case when n1 is significantly gre... | Bootstrapping power estimates for a bootstrap test
I don't think use bootstrap to artificially increase your sample size would be a good idea. Any violation of the assumption of the independence of the observations dramatically increase the odds od a |
37,404 | Bootstrapping power estimates for a bootstrap test | As you bootstrap you assume that the new bootstrapped distribution is equivalent to the original distribution. If $n_1>n_0$ then you are forced to draw repeated values of $n_0$, which leads to several problems. First of all, the bootstrapped distribution will only have de facto $n_0$ values, the rest being copies, and ... | Bootstrapping power estimates for a bootstrap test | As you bootstrap you assume that the new bootstrapped distribution is equivalent to the original distribution. If $n_1>n_0$ then you are forced to draw repeated values of $n_0$, which leads to several | Bootstrapping power estimates for a bootstrap test
As you bootstrap you assume that the new bootstrapped distribution is equivalent to the original distribution. If $n_1>n_0$ then you are forced to draw repeated values of $n_0$, which leads to several problems. First of all, the bootstrapped distribution will only have... | Bootstrapping power estimates for a bootstrap test
As you bootstrap you assume that the new bootstrapped distribution is equivalent to the original distribution. If $n_1>n_0$ then you are forced to draw repeated values of $n_0$, which leads to several |
37,405 | Confusion matrices with percentages rather than number of instances? | A confusion matrix in percents would be appropriate if the distribution between your classes is flat (either naturally, or intentionally sampled that way). If this is not the case, such a confusion matrix can lead to major confusion.
It is useful to have both: number of instances overall (to see the skews) and percents... | Confusion matrices with percentages rather than number of instances? | A confusion matrix in percents would be appropriate if the distribution between your classes is flat (either naturally, or intentionally sampled that way). If this is not the case, such a confusion ma | Confusion matrices with percentages rather than number of instances?
A confusion matrix in percents would be appropriate if the distribution between your classes is flat (either naturally, or intentionally sampled that way). If this is not the case, such a confusion matrix can lead to major confusion.
It is useful to h... | Confusion matrices with percentages rather than number of instances?
A confusion matrix in percents would be appropriate if the distribution between your classes is flat (either naturally, or intentionally sampled that way). If this is not the case, such a confusion ma |
37,406 | Applying Recency In Logistic Regression | It doesn't necessarily have to be arbitrary; you could, for example, assume exponential discounting and look for the coefficient that has the best predictive value.
That is, at time $t$ you might say that the weight, $w_{t-k} \propto p^k$ or equivalently $w_{t-k} \propto \exp{(-\alpha k)}$
(where $0<p<1$ and $\alpha>0... | Applying Recency In Logistic Regression | It doesn't necessarily have to be arbitrary; you could, for example, assume exponential discounting and look for the coefficient that has the best predictive value.
That is, at time $t$ you might say | Applying Recency In Logistic Regression
It doesn't necessarily have to be arbitrary; you could, for example, assume exponential discounting and look for the coefficient that has the best predictive value.
That is, at time $t$ you might say that the weight, $w_{t-k} \propto p^k$ or equivalently $w_{t-k} \propto \exp{(-\... | Applying Recency In Logistic Regression
It doesn't necessarily have to be arbitrary; you could, for example, assume exponential discounting and look for the coefficient that has the best predictive value.
That is, at time $t$ you might say |
37,407 | Imputing a missing variable based on common variables with another data set | In general, I would do something close to your approach I, with some minor tweaks. Assuming that you want eventually to estimate some population parameters on the imputed data, I would use multiple imputation to obtain correct confidence intervals and P-values.
Rounding is generally not recommended, unless you use "ad... | Imputing a missing variable based on common variables with another data set | In general, I would do something close to your approach I, with some minor tweaks. Assuming that you want eventually to estimate some population parameters on the imputed data, I would use multiple im | Imputing a missing variable based on common variables with another data set
In general, I would do something close to your approach I, with some minor tweaks. Assuming that you want eventually to estimate some population parameters on the imputed data, I would use multiple imputation to obtain correct confidence interv... | Imputing a missing variable based on common variables with another data set
In general, I would do something close to your approach I, with some minor tweaks. Assuming that you want eventually to estimate some population parameters on the imputed data, I would use multiple im |
37,408 | Imputing a missing variable based on common variables with another data set | This may not be the exact answer but it does answer what you are looking for. There is a StatMatch package in R that does the parametric and non-parametric matching based on the common variables which is what you may want to look upon. To use the non-parametric approach you have to define the donation class (usually ca... | Imputing a missing variable based on common variables with another data set | This may not be the exact answer but it does answer what you are looking for. There is a StatMatch package in R that does the parametric and non-parametric matching based on the common variables which | Imputing a missing variable based on common variables with another data set
This may not be the exact answer but it does answer what you are looking for. There is a StatMatch package in R that does the parametric and non-parametric matching based on the common variables which is what you may want to look upon. To use t... | Imputing a missing variable based on common variables with another data set
This may not be the exact answer but it does answer what you are looking for. There is a StatMatch package in R that does the parametric and non-parametric matching based on the common variables which |
37,409 | How to use Box-Muller transform to generate n-dimensional normal random variables | Box-Muller generates pairs of independent normals from pairs of independent uniforms.
To get more than two independent normals, generate more uniforms. If you want 17 normals, generate 18 uniforms, and get 9 pairs of normals. Discard one. If your 18 uniforms are independent, your 17 normals should be.
http://en.wikiped... | How to use Box-Muller transform to generate n-dimensional normal random variables | Box-Muller generates pairs of independent normals from pairs of independent uniforms.
To get more than two independent normals, generate more uniforms. If you want 17 normals, generate 18 uniforms, an | How to use Box-Muller transform to generate n-dimensional normal random variables
Box-Muller generates pairs of independent normals from pairs of independent uniforms.
To get more than two independent normals, generate more uniforms. If you want 17 normals, generate 18 uniforms, and get 9 pairs of normals. Discard one.... | How to use Box-Muller transform to generate n-dimensional normal random variables
Box-Muller generates pairs of independent normals from pairs of independent uniforms.
To get more than two independent normals, generate more uniforms. If you want 17 normals, generate 18 uniforms, an |
37,410 | How close to zero should the sum of the random effects be in GLMM (with lme4) | Since @Hemmo's code got slightly mangled in the "Bounty" box, I'm adding this reformatted version as "community wiki". If this is not an appropriate use of the wiki, I apologize in advance. Feel free to remove it.
library(mvabund)
library(lme4)
data(spider)
Y <- as.matrix(spider$abund)
X <- spider$x
X <- X[ ,c(1, 4... | How close to zero should the sum of the random effects be in GLMM (with lme4) | Since @Hemmo's code got slightly mangled in the "Bounty" box, I'm adding this reformatted version as "community wiki". If this is not an appropriate use of the wiki, I apologize in advance. Feel free | How close to zero should the sum of the random effects be in GLMM (with lme4)
Since @Hemmo's code got slightly mangled in the "Bounty" box, I'm adding this reformatted version as "community wiki". If this is not an appropriate use of the wiki, I apologize in advance. Feel free to remove it.
library(mvabund)
library(lm... | How close to zero should the sum of the random effects be in GLMM (with lme4)
Since @Hemmo's code got slightly mangled in the "Bounty" box, I'm adding this reformatted version as "community wiki". If this is not an appropriate use of the wiki, I apologize in advance. Feel free |
37,411 | Distribution of eigenvalues given one is known | Here is a document about your issue: http://math.nyu.edu/faculty/avellane/LalouxPCA.pdf
The idea is simple, you calculate the Marcenko-Pastur distribution with a modified variance of the elements of the matrix. The modified variance simply correspond to the variance explained by other eigenvalue than the first one.
As... | Distribution of eigenvalues given one is known | Here is a document about your issue: http://math.nyu.edu/faculty/avellane/LalouxPCA.pdf
The idea is simple, you calculate the Marcenko-Pastur distribution with a modified variance of the elements of t | Distribution of eigenvalues given one is known
Here is a document about your issue: http://math.nyu.edu/faculty/avellane/LalouxPCA.pdf
The idea is simple, you calculate the Marcenko-Pastur distribution with a modified variance of the elements of the matrix. The modified variance simply correspond to the variance explai... | Distribution of eigenvalues given one is known
Here is a document about your issue: http://math.nyu.edu/faculty/avellane/LalouxPCA.pdf
The idea is simple, you calculate the Marcenko-Pastur distribution with a modified variance of the elements of t |
37,412 | Recommend a graduate-level regression textbook? | I believe Graham Cookson's answer to a similar question would be of assistance. Basically, he recommends Gelman and Hill's Data Analysis Using Regression and Multilevel/Hierarchical Models. According to Mr. Cookson, the book "covers basic regression, multilevel regression, and Bayesian methods in a clear and intuitive ... | Recommend a graduate-level regression textbook? | I believe Graham Cookson's answer to a similar question would be of assistance. Basically, he recommends Gelman and Hill's Data Analysis Using Regression and Multilevel/Hierarchical Models. According | Recommend a graduate-level regression textbook?
I believe Graham Cookson's answer to a similar question would be of assistance. Basically, he recommends Gelman and Hill's Data Analysis Using Regression and Multilevel/Hierarchical Models. According to Mr. Cookson, the book "covers basic regression, multilevel regression... | Recommend a graduate-level regression textbook?
I believe Graham Cookson's answer to a similar question would be of assistance. Basically, he recommends Gelman and Hill's Data Analysis Using Regression and Multilevel/Hierarchical Models. According |
37,413 | Unit root tests and stationarity | The short answer is no at least for the ADF. I suspect a similar reasoning as the one outlined below applies for the KPSS, but I have not investigated this. The reason that the ADF will not work is that it is based on the notion of integration of order d, $d\geq 0$ to capture nonstationarity. (KPSS also is based on thi... | Unit root tests and stationarity | The short answer is no at least for the ADF. I suspect a similar reasoning as the one outlined below applies for the KPSS, but I have not investigated this. The reason that the ADF will not work is th | Unit root tests and stationarity
The short answer is no at least for the ADF. I suspect a similar reasoning as the one outlined below applies for the KPSS, but I have not investigated this. The reason that the ADF will not work is that it is based on the notion of integration of order d, $d\geq 0$ to capture nonstation... | Unit root tests and stationarity
The short answer is no at least for the ADF. I suspect a similar reasoning as the one outlined below applies for the KPSS, but I have not investigated this. The reason that the ADF will not work is th |
37,414 | Regarding the sampling procedure in Adaboost algorithm | There are two methods for training Adaboost. Either use the weight vector directly in the training of the weak learner, or use the weight vector to sample datapoints with replacement from the original data.
In the latter case the sampled dataset is the same size as the original dataset, and will contain some repeated d... | Regarding the sampling procedure in Adaboost algorithm | There are two methods for training Adaboost. Either use the weight vector directly in the training of the weak learner, or use the weight vector to sample datapoints with replacement from the original | Regarding the sampling procedure in Adaboost algorithm
There are two methods for training Adaboost. Either use the weight vector directly in the training of the weak learner, or use the weight vector to sample datapoints with replacement from the original data.
In the latter case the sampled dataset is the same size as... | Regarding the sampling procedure in Adaboost algorithm
There are two methods for training Adaboost. Either use the weight vector directly in the training of the weak learner, or use the weight vector to sample datapoints with replacement from the original |
37,415 | Generalization of multivariate normal distribution and classification | The answer to the first question was given by Procrastinator in a comment: The family is called Elliptical Distributions. The standard textbook reference seems to be
Fang, K., Kotz, S., Ng, K.W., 1990. Symmetric Multivariate and Related
Distributions. Chapman and Hall.
Regarding the second question, it appears that... | Generalization of multivariate normal distribution and classification | The answer to the first question was given by Procrastinator in a comment: The family is called Elliptical Distributions. The standard textbook reference seems to be
Fang, K., Kotz, S., Ng, K.W., 199 | Generalization of multivariate normal distribution and classification
The answer to the first question was given by Procrastinator in a comment: The family is called Elliptical Distributions. The standard textbook reference seems to be
Fang, K., Kotz, S., Ng, K.W., 1990. Symmetric Multivariate and Related
Distributi... | Generalization of multivariate normal distribution and classification
The answer to the first question was given by Procrastinator in a comment: The family is called Elliptical Distributions. The standard textbook reference seems to be
Fang, K., Kotz, S., Ng, K.W., 199 |
37,416 | What are the (best) methods for multiple comparisons correction with bootstrap for multiple glm models? | Method 1: Naive bootstrap
Calculate the $\hat{\vec{\theta}}$ on each bootstrap sample. This way we will know the (hopefully) natural variability in the test statistics.
The adjusted p-value is the 1 - percentage of cases where $\hat{\theta} > \theta_0$ (or $\hat{\theta} < \theta_0$ or $\left|\frac{\hat{\theta}}{\theta... | What are the (best) methods for multiple comparisons correction with bootstrap for multiple glm mode | Method 1: Naive bootstrap
Calculate the $\hat{\vec{\theta}}$ on each bootstrap sample. This way we will know the (hopefully) natural variability in the test statistics.
The adjusted p-value is the 1 | What are the (best) methods for multiple comparisons correction with bootstrap for multiple glm models?
Method 1: Naive bootstrap
Calculate the $\hat{\vec{\theta}}$ on each bootstrap sample. This way we will know the (hopefully) natural variability in the test statistics.
The adjusted p-value is the 1 - percentage of ... | What are the (best) methods for multiple comparisons correction with bootstrap for multiple glm mode
Method 1: Naive bootstrap
Calculate the $\hat{\vec{\theta}}$ on each bootstrap sample. This way we will know the (hopefully) natural variability in the test statistics.
The adjusted p-value is the 1 |
37,417 | Calculating $R^2$ for Elastic Net | Just use the regular $R^2$, i.e. the squared correlation between the fitted and the actual values. Whether the model was fit by OLS or by penalized OLS (such as the elastic net), it will still reflect the proportion of variance explained.
Be aware, however, that model diagnostics and performance measures (such as $R^2$... | Calculating $R^2$ for Elastic Net | Just use the regular $R^2$, i.e. the squared correlation between the fitted and the actual values. Whether the model was fit by OLS or by penalized OLS (such as the elastic net), it will still reflect | Calculating $R^2$ for Elastic Net
Just use the regular $R^2$, i.e. the squared correlation between the fitted and the actual values. Whether the model was fit by OLS or by penalized OLS (such as the elastic net), it will still reflect the proportion of variance explained.
Be aware, however, that model diagnostics and p... | Calculating $R^2$ for Elastic Net
Just use the regular $R^2$, i.e. the squared correlation between the fitted and the actual values. Whether the model was fit by OLS or by penalized OLS (such as the elastic net), it will still reflect |
37,418 | How to identify structural change using a Chow test on Eviews? | I am assuming that you are treating each country separately, and are attempting to determine if there is a break-point in the level of a series. Here are three (EDIT: four) main points that I hope will help:
The Chow test assumes that there is a known break-point in the series. If this point is not know, the Chow test... | How to identify structural change using a Chow test on Eviews? | I am assuming that you are treating each country separately, and are attempting to determine if there is a break-point in the level of a series. Here are three (EDIT: four) main points that I hope wil | How to identify structural change using a Chow test on Eviews?
I am assuming that you are treating each country separately, and are attempting to determine if there is a break-point in the level of a series. Here are three (EDIT: four) main points that I hope will help:
The Chow test assumes that there is a known brea... | How to identify structural change using a Chow test on Eviews?
I am assuming that you are treating each country separately, and are attempting to determine if there is a break-point in the level of a series. Here are three (EDIT: four) main points that I hope wil |
37,419 | How to identify structural change using a Chow test on Eviews? | Chow test tests whether the two different models have the same coefficients. It follows $F$ distribution with $k$ and $N_1+N_2-2k$ degrees of freedom, where $k$ is the number of parameters and $N_1$ and $N_2$ are sample sizes of the data the two models are estimated on.
In your case two models are the same regressio... | How to identify structural change using a Chow test on Eviews? | Chow test tests whether the two different models have the same coefficients. It follows $F$ distribution with $k$ and $N_1+N_2-2k$ degrees of freedom, where $k$ is the number of parameters and $N_1$ a | How to identify structural change using a Chow test on Eviews?
Chow test tests whether the two different models have the same coefficients. It follows $F$ distribution with $k$ and $N_1+N_2-2k$ degrees of freedom, where $k$ is the number of parameters and $N_1$ and $N_2$ are sample sizes of the data the two models are ... | How to identify structural change using a Chow test on Eviews?
Chow test tests whether the two different models have the same coefficients. It follows $F$ distribution with $k$ and $N_1+N_2-2k$ degrees of freedom, where $k$ is the number of parameters and $N_1$ a |
37,420 | How to identify structural change using a Chow test on Eviews? | here the null hypothesis is H0: no structural break
just look at p-value of f-statistics it is 0.0016 which is below 5% So reject H0 and there is structural break in your data.
thx | How to identify structural change using a Chow test on Eviews? | here the null hypothesis is H0: no structural break
just look at p-value of f-statistics it is 0.0016 which is below 5% So reject H0 and there is structural break in your data.
thx | How to identify structural change using a Chow test on Eviews?
here the null hypothesis is H0: no structural break
just look at p-value of f-statistics it is 0.0016 which is below 5% So reject H0 and there is structural break in your data.
thx | How to identify structural change using a Chow test on Eviews?
here the null hypothesis is H0: no structural break
just look at p-value of f-statistics it is 0.0016 which is below 5% So reject H0 and there is structural break in your data.
thx |
37,421 | How to identify structural change using a Chow test on Eviews? | Ho: Parameter is structurally stable when your probability is less than 5% as in your case it is 0.0016 then do reject Ho (i.e., the null hypothesis). It means there is a structural break in your data. The best thing you can do is check each variable one by one by using the chow test. | How to identify structural change using a Chow test on Eviews? | Ho: Parameter is structurally stable when your probability is less than 5% as in your case it is 0.0016 then do reject Ho (i.e., the null hypothesis). It means there is a structural break in your dat | How to identify structural change using a Chow test on Eviews?
Ho: Parameter is structurally stable when your probability is less than 5% as in your case it is 0.0016 then do reject Ho (i.e., the null hypothesis). It means there is a structural break in your data. The best thing you can do is check each variable one b... | How to identify structural change using a Chow test on Eviews?
Ho: Parameter is structurally stable when your probability is less than 5% as in your case it is 0.0016 then do reject Ho (i.e., the null hypothesis). It means there is a structural break in your dat |
37,422 | How to identify structural change using a Chow test on Eviews? | I think that Ho is proved and H1 disproved. In addition the GDP has no unit root.
is meaning the trend remains. There is no break point. | How to identify structural change using a Chow test on Eviews? | I think that Ho is proved and H1 disproved. In addition the GDP has no unit root.
is meaning the trend remains. There is no break point. | How to identify structural change using a Chow test on Eviews?
I think that Ho is proved and H1 disproved. In addition the GDP has no unit root.
is meaning the trend remains. There is no break point. | How to identify structural change using a Chow test on Eviews?
I think that Ho is proved and H1 disproved. In addition the GDP has no unit root.
is meaning the trend remains. There is no break point. |
37,423 | How to identify structural change using a Chow test on Eviews? | Dear Stability test is used to determine the stability of coefficients of independent variable after OLS is peroformed. Stability test is performed by CUSUM and CUSUMQ. but if results of both contradict each other , then Chow breakpoint is used as alternative.
for stability test Ho : Coefficients are stable or Coeffic... | How to identify structural change using a Chow test on Eviews? | Dear Stability test is used to determine the stability of coefficients of independent variable after OLS is peroformed. Stability test is performed by CUSUM and CUSUMQ. but if results of both contrad | How to identify structural change using a Chow test on Eviews?
Dear Stability test is used to determine the stability of coefficients of independent variable after OLS is peroformed. Stability test is performed by CUSUM and CUSUMQ. but if results of both contradict each other , then Chow breakpoint is used as alternat... | How to identify structural change using a Chow test on Eviews?
Dear Stability test is used to determine the stability of coefficients of independent variable after OLS is peroformed. Stability test is performed by CUSUM and CUSUMQ. but if results of both contrad |
37,424 | How to identify structural change using a Chow test on Eviews? | An alternative for finding the exact year of structural break is to preform the
regression:
$$yt=β0+β_1t+γ_0Dt+γ_1tDt+νt(2)\;.$$ Move on the number of cases one by one below. From the point of structural break the regression coefficients will become significant continuously. | How to identify structural change using a Chow test on Eviews? | An alternative for finding the exact year of structural break is to preform the
regression:
$$yt=β0+β_1t+γ_0Dt+γ_1tDt+νt(2)\;.$$ Move on the number of cases one by one below. From the point of struc | How to identify structural change using a Chow test on Eviews?
An alternative for finding the exact year of structural break is to preform the
regression:
$$yt=β0+β_1t+γ_0Dt+γ_1tDt+νt(2)\;.$$ Move on the number of cases one by one below. From the point of structural break the regression coefficients will become signi... | How to identify structural change using a Chow test on Eviews?
An alternative for finding the exact year of structural break is to preform the
regression:
$$yt=β0+β_1t+γ_0Dt+γ_1tDt+νt(2)\;.$$ Move on the number of cases one by one below. From the point of struc |
37,425 | Standard normal distribution on a subspace | Yes. You have that $U$ is a subspace of $\mathbb R^n$. Let $Y \sim \text{N}(0,I)$ and $P$ be the orthogonal projection matrix on $U$, so that $P$ is symmetric and idempotent. Then $PY \sim \text{N}(P0,PIP^T) = \text{N}(0,P)$. This is a singular normal distribution, which on the subspace $U$ is the standard normal on th... | Standard normal distribution on a subspace | Yes. You have that $U$ is a subspace of $\mathbb R^n$. Let $Y \sim \text{N}(0,I)$ and $P$ be the orthogonal projection matrix on $U$, so that $P$ is symmetric and idempotent. Then $PY \sim \text{N}(P0 | Standard normal distribution on a subspace
Yes. You have that $U$ is a subspace of $\mathbb R^n$. Let $Y \sim \text{N}(0,I)$ and $P$ be the orthogonal projection matrix on $U$, so that $P$ is symmetric and idempotent. Then $PY \sim \text{N}(P0,PIP^T) = \text{N}(0,P)$. This is a singular normal distribution, which on th... | Standard normal distribution on a subspace
Yes. You have that $U$ is a subspace of $\mathbb R^n$. Let $Y \sim \text{N}(0,I)$ and $P$ be the orthogonal projection matrix on $U$, so that $P$ is symmetric and idempotent. Then $PY \sim \text{N}(P0 |
37,426 | Repeated measures: aggregate, fixed effects, or random effects? | I think you should go with subject as a fixed effect since you only have 3 subjects.
What is the goal of the study? If you want to test whether all 8 treatments are the same - or test particular contrasts - you could treat subject like a fixed effect block. If you want to predict the response of an as-yet-unseen subjec... | Repeated measures: aggregate, fixed effects, or random effects? | I think you should go with subject as a fixed effect since you only have 3 subjects.
What is the goal of the study? If you want to test whether all 8 treatments are the same - or test particular contr | Repeated measures: aggregate, fixed effects, or random effects?
I think you should go with subject as a fixed effect since you only have 3 subjects.
What is the goal of the study? If you want to test whether all 8 treatments are the same - or test particular contrasts - you could treat subject like a fixed effect block... | Repeated measures: aggregate, fixed effects, or random effects?
I think you should go with subject as a fixed effect since you only have 3 subjects.
What is the goal of the study? If you want to test whether all 8 treatments are the same - or test particular contr |
37,427 | What am I measuring when I apply a graded response model to the "Hunting of the Snark" dataset? | The analyses you did are somewhat unorthodox. Using the GRM you usually want to locate each comment on a latent unidimensional trait from unfriendly to friendly. Therefore, one would use several polytomous items which are indicators of the latent trait. In your analysis, you treated the different raters as "items".
Plo... | What am I measuring when I apply a graded response model to the "Hunting of the Snark" dataset? | The analyses you did are somewhat unorthodox. Using the GRM you usually want to locate each comment on a latent unidimensional trait from unfriendly to friendly. Therefore, one would use several polyt | What am I measuring when I apply a graded response model to the "Hunting of the Snark" dataset?
The analyses you did are somewhat unorthodox. Using the GRM you usually want to locate each comment on a latent unidimensional trait from unfriendly to friendly. Therefore, one would use several polytomous items which are in... | What am I measuring when I apply a graded response model to the "Hunting of the Snark" dataset?
The analyses you did are somewhat unorthodox. Using the GRM you usually want to locate each comment on a latent unidimensional trait from unfriendly to friendly. Therefore, one would use several polyt |
37,428 | Bootstrapping and comparing multiple proportions | The questions, especially the second one, are meaningless as they stand right now. The problem is that the concept of "taking a random number of candies from the bag" is not defined. Even with a finite number of candies in the bag there could be multiple definitions. For example, the following two both sound reasonable... | Bootstrapping and comparing multiple proportions | The questions, especially the second one, are meaningless as they stand right now. The problem is that the concept of "taking a random number of candies from the bag" is not defined. Even with a finit | Bootstrapping and comparing multiple proportions
The questions, especially the second one, are meaningless as they stand right now. The problem is that the concept of "taking a random number of candies from the bag" is not defined. Even with a finite number of candies in the bag there could be multiple definitions. For... | Bootstrapping and comparing multiple proportions
The questions, especially the second one, are meaningless as they stand right now. The problem is that the concept of "taking a random number of candies from the bag" is not defined. Even with a finit |
37,429 | Bootstrapping and comparing multiple proportions | The way you've constructed your problem you can't really get the answers you want, even with bootstrapping. I'll give you responses from the bootstrap perspective.
#1 All you can conclude is that she was equally generous. It's because you only have one sample of the random process, the one for kids. Extrapolating th... | Bootstrapping and comparing multiple proportions | The way you've constructed your problem you can't really get the answers you want, even with bootstrapping. I'll give you responses from the bootstrap perspective.
#1 All you can conclude is that she | Bootstrapping and comparing multiple proportions
The way you've constructed your problem you can't really get the answers you want, even with bootstrapping. I'll give you responses from the bootstrap perspective.
#1 All you can conclude is that she was equally generous. It's because you only have one sample of the ra... | Bootstrapping and comparing multiple proportions
The way you've constructed your problem you can't really get the answers you want, even with bootstrapping. I'll give you responses from the bootstrap perspective.
#1 All you can conclude is that she |
37,430 | Bootstrapping and comparing multiple proportions | The bootstrap is not needed here because the null distribution is well defined. Bootstrap should not be used just because you are comfortable with simulation. The bootstrap works in this situation and will give essentally the same results as the straightforward method. So your motivation is not a factor here. Sound... | Bootstrapping and comparing multiple proportions | The bootstrap is not needed here because the null distribution is well defined. Bootstrap should not be used just because you are comfortable with simulation. The bootstrap works in this situation a | Bootstrapping and comparing multiple proportions
The bootstrap is not needed here because the null distribution is well defined. Bootstrap should not be used just because you are comfortable with simulation. The bootstrap works in this situation and will give essentally the same results as the straightforward method.... | Bootstrapping and comparing multiple proportions
The bootstrap is not needed here because the null distribution is well defined. Bootstrap should not be used just because you are comfortable with simulation. The bootstrap works in this situation a |
37,431 | How to measure the number of people in a picture of a crowd? | I am working in a similar project, I will follow the next approach:
Get a lot of classified images, images with few people, images with crowded spaces. For example, 'The Zócalo' in Mexico, City could afford more or less 250 000 people.
Extract features from these images, maybe with methods like either HOG or SIFT. HOG... | How to measure the number of people in a picture of a crowd? | I am working in a similar project, I will follow the next approach:
Get a lot of classified images, images with few people, images with crowded spaces. For example, 'The Zócalo' in Mexico, City could | How to measure the number of people in a picture of a crowd?
I am working in a similar project, I will follow the next approach:
Get a lot of classified images, images with few people, images with crowded spaces. For example, 'The Zócalo' in Mexico, City could afford more or less 250 000 people.
Extract features from ... | How to measure the number of people in a picture of a crowd?
I am working in a similar project, I will follow the next approach:
Get a lot of classified images, images with few people, images with crowded spaces. For example, 'The Zócalo' in Mexico, City could |
37,432 | Autocorrelation from multiple time series samples | Yes, there is a correct way and it's simple, too.
By definition, the autocorrelation of a stationary process $X_t$ at lag $dt$ is the correlation between $X_t$ and $X_{t+dt}$. Suppose you have observations of this process $x_{t_0}, x_{t_0+dt}, x_{t_0+2dt}, \ldots, x_{t_0+k_0dt}$ at lag $dt$, another set of observation... | Autocorrelation from multiple time series samples | Yes, there is a correct way and it's simple, too.
By definition, the autocorrelation of a stationary process $X_t$ at lag $dt$ is the correlation between $X_t$ and $X_{t+dt}$. Suppose you have observ | Autocorrelation from multiple time series samples
Yes, there is a correct way and it's simple, too.
By definition, the autocorrelation of a stationary process $X_t$ at lag $dt$ is the correlation between $X_t$ and $X_{t+dt}$. Suppose you have observations of this process $x_{t_0}, x_{t_0+dt}, x_{t_0+2dt}, \ldots, x_{t... | Autocorrelation from multiple time series samples
Yes, there is a correct way and it's simple, too.
By definition, the autocorrelation of a stationary process $X_t$ at lag $dt$ is the correlation between $X_t$ and $X_{t+dt}$. Suppose you have observ |
37,433 | Autocorrelation from multiple time series samples | First of all either way you do it you are assuming each day is the same as any other from 12 noon to 3 PM. Also in time series analysis it is not common to have ensembles of the process. But given the assumptions I think you can treat it like you would with independent individual observations.
Each day can be viewed as... | Autocorrelation from multiple time series samples | First of all either way you do it you are assuming each day is the same as any other from 12 noon to 3 PM. Also in time series analysis it is not common to have ensembles of the process. But given the | Autocorrelation from multiple time series samples
First of all either way you do it you are assuming each day is the same as any other from 12 noon to 3 PM. Also in time series analysis it is not common to have ensembles of the process. But given the assumptions I think you can treat it like you would with independent ... | Autocorrelation from multiple time series samples
First of all either way you do it you are assuming each day is the same as any other from 12 noon to 3 PM. Also in time series analysis it is not common to have ensembles of the process. But given the |
37,434 | Autocorrelation from multiple time series samples | I have stumbled across the same type of problem and I know that people have used your first suggestion in a situation where each sample is a number of residuals from one individual (in mixed effects modelling). See this paper.
The autocorrelation is merely a general measure of dependence between adjacent sample points.... | Autocorrelation from multiple time series samples | I have stumbled across the same type of problem and I know that people have used your first suggestion in a situation where each sample is a number of residuals from one individual (in mixed effects m | Autocorrelation from multiple time series samples
I have stumbled across the same type of problem and I know that people have used your first suggestion in a situation where each sample is a number of residuals from one individual (in mixed effects modelling). See this paper.
The autocorrelation is merely a general mea... | Autocorrelation from multiple time series samples
I have stumbled across the same type of problem and I know that people have used your first suggestion in a situation where each sample is a number of residuals from one individual (in mixed effects m |
37,435 | Model-selection for linear mixed models over alternative sets of parameters (nlme function in R) | I would use Akaike’s Information Criterion ($AIC$) for model selection where:
$$
AIC = -2ln(L)-2k
$$
Though a better alternative is often $AIC_c$, which is the second-order Akaike’s Information Criterion ($AIC_c$). $AIC_c$ is corrected for small sample size with an addtion bias-correction term because $AIC$ can perfor... | Model-selection for linear mixed models over alternative sets of parameters (nlme function in R) | I would use Akaike’s Information Criterion ($AIC$) for model selection where:
$$
AIC = -2ln(L)-2k
$$
Though a better alternative is often $AIC_c$, which is the second-order Akaike’s Information Criter | Model-selection for linear mixed models over alternative sets of parameters (nlme function in R)
I would use Akaike’s Information Criterion ($AIC$) for model selection where:
$$
AIC = -2ln(L)-2k
$$
Though a better alternative is often $AIC_c$, which is the second-order Akaike’s Information Criterion ($AIC_c$). $AIC_c$... | Model-selection for linear mixed models over alternative sets of parameters (nlme function in R)
I would use Akaike’s Information Criterion ($AIC$) for model selection where:
$$
AIC = -2ln(L)-2k
$$
Though a better alternative is often $AIC_c$, which is the second-order Akaike’s Information Criter |
37,436 | Generalization of Brownian motion to $\alpha$-stable distributions | My quick answer would be yes, but I am not sure about the scale parameter. You can view a Gaussian random walk as a subset of random walks with stable distributions. All stable distributions have the property that a linear combination of two i.i.d. stable distributions is also stable. (All this is related to a general... | Generalization of Brownian motion to $\alpha$-stable distributions | My quick answer would be yes, but I am not sure about the scale parameter. You can view a Gaussian random walk as a subset of random walks with stable distributions. All stable distributions have the | Generalization of Brownian motion to $\alpha$-stable distributions
My quick answer would be yes, but I am not sure about the scale parameter. You can view a Gaussian random walk as a subset of random walks with stable distributions. All stable distributions have the property that a linear combination of two i.i.d. sta... | Generalization of Brownian motion to $\alpha$-stable distributions
My quick answer would be yes, but I am not sure about the scale parameter. You can view a Gaussian random walk as a subset of random walks with stable distributions. All stable distributions have the |
37,437 | Linear regression with shot noise | The probability model for such shot noise is
$$X \sim \text{Poisson}(\mu),\quad Y|X \sim \text{Normal}(\beta_0+\beta_1 X, \sigma^2).$$
A good estimate of $\mu$ is the mean of $X$ and a good estimate of $(\beta_0, \beta_1)$ is afforded by ordinary least squares, because the values of $Y$ are assumed independent, identic... | Linear regression with shot noise | The probability model for such shot noise is
$$X \sim \text{Poisson}(\mu),\quad Y|X \sim \text{Normal}(\beta_0+\beta_1 X, \sigma^2).$$
A good estimate of $\mu$ is the mean of $X$ and a good estimate o | Linear regression with shot noise
The probability model for such shot noise is
$$X \sim \text{Poisson}(\mu),\quad Y|X \sim \text{Normal}(\beta_0+\beta_1 X, \sigma^2).$$
A good estimate of $\mu$ is the mean of $X$ and a good estimate of $(\beta_0, \beta_1)$ is afforded by ordinary least squares, because the values of $Y... | Linear regression with shot noise
The probability model for such shot noise is
$$X \sim \text{Poisson}(\mu),\quad Y|X \sim \text{Normal}(\beta_0+\beta_1 X, \sigma^2).$$
A good estimate of $\mu$ is the mean of $X$ and a good estimate o |
37,438 | Combining LASSO coefficients across imputed datasets | I am by no means an expert, but found this while looking into this problem for my own work.
https://www.biostat.wisc.edu/sites/default/files/tr_217.pdf
In a nutshell, they used grouped lasso (reference below) on the variables, where a "group" of variables actually refers to the same variable, but "grouped" across the ... | Combining LASSO coefficients across imputed datasets | I am by no means an expert, but found this while looking into this problem for my own work.
https://www.biostat.wisc.edu/sites/default/files/tr_217.pdf
In a nutshell, they used grouped lasso (referen | Combining LASSO coefficients across imputed datasets
I am by no means an expert, but found this while looking into this problem for my own work.
https://www.biostat.wisc.edu/sites/default/files/tr_217.pdf
In a nutshell, they used grouped lasso (reference below) on the variables, where a "group" of variables actually r... | Combining LASSO coefficients across imputed datasets
I am by no means an expert, but found this while looking into this problem for my own work.
https://www.biostat.wisc.edu/sites/default/files/tr_217.pdf
In a nutshell, they used grouped lasso (referen |
37,439 | Bayesian inference on a sum of iid real-valued random variables | Consider the following Bayesian nonparametric analysis.
Define $\mathscr{X}=[0,1]$ and let $\mathscr{B}$ be the Borel subsets of $\mathscr{X}$. Let $\alpha$ be a nonzero finite measure over $(\mathscr{X},\mathscr{B})$.
Let $Q$ be a Dirichlet process with parameter $\alpha$, and suppose that $X_1,\dots,X_n$ are conditio... | Bayesian inference on a sum of iid real-valued random variables | Consider the following Bayesian nonparametric analysis.
Define $\mathscr{X}=[0,1]$ and let $\mathscr{B}$ be the Borel subsets of $\mathscr{X}$. Let $\alpha$ be a nonzero finite measure over $(\mathscr | Bayesian inference on a sum of iid real-valued random variables
Consider the following Bayesian nonparametric analysis.
Define $\mathscr{X}=[0,1]$ and let $\mathscr{B}$ be the Borel subsets of $\mathscr{X}$. Let $\alpha$ be a nonzero finite measure over $(\mathscr{X},\mathscr{B})$.
Let $Q$ be a Dirichlet process with p... | Bayesian inference on a sum of iid real-valued random variables
Consider the following Bayesian nonparametric analysis.
Define $\mathscr{X}=[0,1]$ and let $\mathscr{B}$ be the Borel subsets of $\mathscr{X}$. Let $\alpha$ be a nonzero finite measure over $(\mathscr |
37,440 | Bayesian inference on a sum of iid real-valued random variables | Forgive the lack of measure theory and abuses of notation in the below...
Since this is Bayesian inference, there must be some prior on the unknown in the problem, which in this case is the distribution of $X_1$, an infinite-dimensional parameter taking values in the set of distributions on $[0, 1]$ (call it $\pi$). Th... | Bayesian inference on a sum of iid real-valued random variables | Forgive the lack of measure theory and abuses of notation in the below...
Since this is Bayesian inference, there must be some prior on the unknown in the problem, which in this case is the distributi | Bayesian inference on a sum of iid real-valued random variables
Forgive the lack of measure theory and abuses of notation in the below...
Since this is Bayesian inference, there must be some prior on the unknown in the problem, which in this case is the distribution of $X_1$, an infinite-dimensional parameter taking va... | Bayesian inference on a sum of iid real-valued random variables
Forgive the lack of measure theory and abuses of notation in the below...
Since this is Bayesian inference, there must be some prior on the unknown in the problem, which in this case is the distributi |
37,441 | Bayesian inference on a sum of iid real-valued random variables | Let each $X_i$ belong to distribution family $F$ and have parameters $\theta$.
Given, $S_k$, we have a distribution on $\theta$:
\begin{align}
\Pr(\theta \mid S_k) &= \frac1Z \Pr(\theta)\Pr(S_k \mid \theta)
\end{align}
And, our distribution on $S_n$, $n \ge k$ is
\begin{align}
\Pr(S_n = i \mid S_k) &= \Pr(S_{n-k} = i -... | Bayesian inference on a sum of iid real-valued random variables | Let each $X_i$ belong to distribution family $F$ and have parameters $\theta$.
Given, $S_k$, we have a distribution on $\theta$:
\begin{align}
\Pr(\theta \mid S_k) &= \frac1Z \Pr(\theta)\Pr(S_k \mid \ | Bayesian inference on a sum of iid real-valued random variables
Let each $X_i$ belong to distribution family $F$ and have parameters $\theta$.
Given, $S_k$, we have a distribution on $\theta$:
\begin{align}
\Pr(\theta \mid S_k) &= \frac1Z \Pr(\theta)\Pr(S_k \mid \theta)
\end{align}
And, our distribution on $S_n$, $n \g... | Bayesian inference on a sum of iid real-valued random variables
Let each $X_i$ belong to distribution family $F$ and have parameters $\theta$.
Given, $S_k$, we have a distribution on $\theta$:
\begin{align}
\Pr(\theta \mid S_k) &= \frac1Z \Pr(\theta)\Pr(S_k \mid \ |
37,442 | Open-sourced pairwise learning models | The R implementation of sofia-ml, RSofia provides a couple of options, rank and query-norm-rank, that allow for pairwise learning. It's a fast SVM implementation out of Google. It's more barebones than some other R packages, but once you get it up and running I've found that it is a nice SVM option. | Open-sourced pairwise learning models | The R implementation of sofia-ml, RSofia provides a couple of options, rank and query-norm-rank, that allow for pairwise learning. It's a fast SVM implementation out of Google. It's more barebones tha | Open-sourced pairwise learning models
The R implementation of sofia-ml, RSofia provides a couple of options, rank and query-norm-rank, that allow for pairwise learning. It's a fast SVM implementation out of Google. It's more barebones than some other R packages, but once you get it up and running I've found that it is ... | Open-sourced pairwise learning models
The R implementation of sofia-ml, RSofia provides a couple of options, rank and query-norm-rank, that allow for pairwise learning. It's a fast SVM implementation out of Google. It's more barebones tha |
37,443 | Is the variance of the multivariate folded normal distribution known? | There is a section entitled 'Bivariate Half-normal distribution in:
Continuous Multivariate Distributions: Models and applications
By Samuel Kotz, Norman Lloyd Johnson, N. Balakrishnan.
I would be curious to see how this can be generalized to a random vector of any dimensions.
In fact, the bivariate case appears to be ... | Is the variance of the multivariate folded normal distribution known? | There is a section entitled 'Bivariate Half-normal distribution in:
Continuous Multivariate Distributions: Models and applications
By Samuel Kotz, Norman Lloyd Johnson, N. Balakrishnan.
I would be cur | Is the variance of the multivariate folded normal distribution known?
There is a section entitled 'Bivariate Half-normal distribution in:
Continuous Multivariate Distributions: Models and applications
By Samuel Kotz, Norman Lloyd Johnson, N. Balakrishnan.
I would be curious to see how this can be generalized to a rando... | Is the variance of the multivariate folded normal distribution known?
There is a section entitled 'Bivariate Half-normal distribution in:
Continuous Multivariate Distributions: Models and applications
By Samuel Kotz, Norman Lloyd Johnson, N. Balakrishnan.
I would be cur |
37,444 | Is the variance of the multivariate folded normal distribution known? | I don't know what you mean by folded normal distribution. The distribution of $|X|$ where $X \sim N(0,1)$? The distribution of $|X|$ when $X \sim N(\mu,\sigma^2)$? But, regardless of the interpretation, if you aver that "The mean and variance of the folded normal
distribution are known" to you, then rest assured tha... | Is the variance of the multivariate folded normal distribution known? | I don't know what you mean by folded normal distribution. The distribution of $|X|$ where $X \sim N(0,1)$? The distribution of $|X|$ when $X \sim N(\mu,\sigma^2)$? But, regardless of the interpretat | Is the variance of the multivariate folded normal distribution known?
I don't know what you mean by folded normal distribution. The distribution of $|X|$ where $X \sim N(0,1)$? The distribution of $|X|$ when $X \sim N(\mu,\sigma^2)$? But, regardless of the interpretation, if you aver that "The mean and variance of th... | Is the variance of the multivariate folded normal distribution known?
I don't know what you mean by folded normal distribution. The distribution of $|X|$ where $X \sim N(0,1)$? The distribution of $|X|$ when $X \sim N(\mu,\sigma^2)$? But, regardless of the interpretat |
37,445 | How to analyze GEE with unevenly spaced observations? | When using the geeglm fonction (in geepack), you can specify the order and the spacing between observations with the waves argument. | How to analyze GEE with unevenly spaced observations? | When using the geeglm fonction (in geepack), you can specify the order and the spacing between observations with the waves argument. | How to analyze GEE with unevenly spaced observations?
When using the geeglm fonction (in geepack), you can specify the order and the spacing between observations with the waves argument. | How to analyze GEE with unevenly spaced observations?
When using the geeglm fonction (in geepack), you can specify the order and the spacing between observations with the waves argument. |
37,446 | How to compute asymptotic confidence intervals for differences in quantiles? | It doesn't sound like you want the difference in quantiles: the parameter you describe is the probability of the interval $[1,2]$. More generally, you can specify any interval endpoints $[x_-, x_+]$ in advance. Given parameters $(\mu, \sigma)$ of the distribution with CDF $F_{(\mu, \sigma)}$, this probability would b... | How to compute asymptotic confidence intervals for differences in quantiles? | It doesn't sound like you want the difference in quantiles: the parameter you describe is the probability of the interval $[1,2]$. More generally, you can specify any interval endpoints $[x_-, x_+]$ | How to compute asymptotic confidence intervals for differences in quantiles?
It doesn't sound like you want the difference in quantiles: the parameter you describe is the probability of the interval $[1,2]$. More generally, you can specify any interval endpoints $[x_-, x_+]$ in advance. Given parameters $(\mu, \sigma... | How to compute asymptotic confidence intervals for differences in quantiles?
It doesn't sound like you want the difference in quantiles: the parameter you describe is the probability of the interval $[1,2]$. More generally, you can specify any interval endpoints $[x_-, x_+]$ |
37,447 | Longitudinal models in R and WINBUGS or JAGS | Longitudinal and mixed models in BUGS is talked about in Ch. 10 of Bayesian Ideas and Data Analysis. Below is a link to the book website which has some example code.
http://www.ics.uci.edu/~wjohnson/BIDA/BIDABook.html | Longitudinal models in R and WINBUGS or JAGS | Longitudinal and mixed models in BUGS is talked about in Ch. 10 of Bayesian Ideas and Data Analysis. Below is a link to the book website which has some example code.
http://www.ics.uci.edu/~wjohnson/ | Longitudinal models in R and WINBUGS or JAGS
Longitudinal and mixed models in BUGS is talked about in Ch. 10 of Bayesian Ideas and Data Analysis. Below is a link to the book website which has some example code.
http://www.ics.uci.edu/~wjohnson/BIDA/BIDABook.html | Longitudinal models in R and WINBUGS or JAGS
Longitudinal and mixed models in BUGS is talked about in Ch. 10 of Bayesian Ideas and Data Analysis. Below is a link to the book website which has some example code.
http://www.ics.uci.edu/~wjohnson/ |
37,448 | Longitudinal models in R and WINBUGS or JAGS | I'm not sure what you mean by R not having "factor analytic models for covariance matrices" - can you clarify what you'd like to reproduce from SAS? To my knowledge this is feasible with a lot of different packages in R.
Regarding antedependence models, there is a book on this very topic that has associated R code and... | Longitudinal models in R and WINBUGS or JAGS | I'm not sure what you mean by R not having "factor analytic models for covariance matrices" - can you clarify what you'd like to reproduce from SAS? To my knowledge this is feasible with a lot of dif | Longitudinal models in R and WINBUGS or JAGS
I'm not sure what you mean by R not having "factor analytic models for covariance matrices" - can you clarify what you'd like to reproduce from SAS? To my knowledge this is feasible with a lot of different packages in R.
Regarding antedependence models, there is a book on t... | Longitudinal models in R and WINBUGS or JAGS
I'm not sure what you mean by R not having "factor analytic models for covariance matrices" - can you clarify what you'd like to reproduce from SAS? To my knowledge this is feasible with a lot of dif |
37,449 | Longitudinal models in R and WINBUGS or JAGS | I believe, with a slight learning curve, you could use one of the SEM packages in R: lavaan, OpenMX, or sem. I am just learning about SEM and these packages, but it does look to me that lavaan has a formula syntax that's much like other modeling (lm, lmer) in R, and SEM lets you do a lot of things with your covariance ... | Longitudinal models in R and WINBUGS or JAGS | I believe, with a slight learning curve, you could use one of the SEM packages in R: lavaan, OpenMX, or sem. I am just learning about SEM and these packages, but it does look to me that lavaan has a f | Longitudinal models in R and WINBUGS or JAGS
I believe, with a slight learning curve, you could use one of the SEM packages in R: lavaan, OpenMX, or sem. I am just learning about SEM and these packages, but it does look to me that lavaan has a formula syntax that's much like other modeling (lm, lmer) in R, and SEM lets... | Longitudinal models in R and WINBUGS or JAGS
I believe, with a slight learning curve, you could use one of the SEM packages in R: lavaan, OpenMX, or sem. I am just learning about SEM and these packages, but it does look to me that lavaan has a f |
37,450 | A name for this distributional condition? | This is almost the condition for the cumulative distribution function to be log-concave , which is a very useful property with many applications. But almost.
A function $F(x)$ is log-concave if
$$\frac {\partial^2 \ln F(x)}{\partial x^2} \le 0 \Rightarrow F''(x)F(x) - \left[F'(x)\right]^2 \le 0$$
Write $\phi(x)$ in ter... | A name for this distributional condition? | This is almost the condition for the cumulative distribution function to be log-concave , which is a very useful property with many applications. But almost.
A function $F(x)$ is log-concave if
$$\fra | A name for this distributional condition?
This is almost the condition for the cumulative distribution function to be log-concave , which is a very useful property with many applications. But almost.
A function $F(x)$ is log-concave if
$$\frac {\partial^2 \ln F(x)}{\partial x^2} \le 0 \Rightarrow F''(x)F(x) - \left[F'(... | A name for this distributional condition?
This is almost the condition for the cumulative distribution function to be log-concave , which is a very useful property with many applications. But almost.
A function $F(x)$ is log-concave if
$$\fra |
37,451 | Sequential Monte Carlo (particle filter) with Metropolis-Hastings weighting | The answer is in the title: this is called sequential Monte Carlo or particle filtering or population Monte Carlo. It is validated in wider generality as an iterated importance sampling scheme where each importance sample is used to generate the following sample. This is for instance covered in Chapter 14 of our book M... | Sequential Monte Carlo (particle filter) with Metropolis-Hastings weighting | The answer is in the title: this is called sequential Monte Carlo or particle filtering or population Monte Carlo. It is validated in wider generality as an iterated importance sampling scheme where e | Sequential Monte Carlo (particle filter) with Metropolis-Hastings weighting
The answer is in the title: this is called sequential Monte Carlo or particle filtering or population Monte Carlo. It is validated in wider generality as an iterated importance sampling scheme where each importance sample is used to generate th... | Sequential Monte Carlo (particle filter) with Metropolis-Hastings weighting
The answer is in the title: this is called sequential Monte Carlo or particle filtering or population Monte Carlo. It is validated in wider generality as an iterated importance sampling scheme where e |
37,452 | Why do these 2 approaches to applying mixed models yield different results? | It's not surprising to see such a difference with lmer or lme. A simple model with a random intercept (e.g., (1|id) in your case) sometimes may fail to fully capture the random effects. To see why this happens, let me use a much simpler dataset than yours to demonstrate the subtle difference. With the data 'dat' from t... | Why do these 2 approaches to applying mixed models yield different results? | It's not surprising to see such a difference with lmer or lme. A simple model with a random intercept (e.g., (1|id) in your case) sometimes may fail to fully capture the random effects. To see why thi | Why do these 2 approaches to applying mixed models yield different results?
It's not surprising to see such a difference with lmer or lme. A simple model with a random intercept (e.g., (1|id) in your case) sometimes may fail to fully capture the random effects. To see why this happens, let me use a much simpler dataset... | Why do these 2 approaches to applying mixed models yield different results?
It's not surprising to see such a difference with lmer or lme. A simple model with a random intercept (e.g., (1|id) in your case) sometimes may fail to fully capture the random effects. To see why thi |
37,453 | Minimum number of observations per variable for linear regression or MARS model | I've often heard of 10 cases per variable as a rule of thumb. It is not clear if this is to mean that you start at 10 cases with 1 covariate, or 20 cases (since you also loose a degree of freedom due to the intercept). I scanned the indexes to a few of my old stats books, and didn't find any reference to a place wher... | Minimum number of observations per variable for linear regression or MARS model | I've often heard of 10 cases per variable as a rule of thumb. It is not clear if this is to mean that you start at 10 cases with 1 covariate, or 20 cases (since you also loose a degree of freedom due | Minimum number of observations per variable for linear regression or MARS model
I've often heard of 10 cases per variable as a rule of thumb. It is not clear if this is to mean that you start at 10 cases with 1 covariate, or 20 cases (since you also loose a degree of freedom due to the intercept). I scanned the index... | Minimum number of observations per variable for linear regression or MARS model
I've often heard of 10 cases per variable as a rule of thumb. It is not clear if this is to mean that you start at 10 cases with 1 covariate, or 20 cases (since you also loose a degree of freedom due |
37,454 | How to test whether variance explained by first factor of PCA differs across repeated measures conditions? | Just one (maybe silly) idea. Save 1st principal component scores variable for condition A (PC1A) and 1st principal component scores variable for condition B (PC1B). The scores should be "raw", that is, their variances or sum-of-squares equal to their eigenvalues. Then use Pitman's test to compare the variances. | How to test whether variance explained by first factor of PCA differs across repeated measures condi | Just one (maybe silly) idea. Save 1st principal component scores variable for condition A (PC1A) and 1st principal component scores variable for condition B (PC1B). The scores should be "raw", that is | How to test whether variance explained by first factor of PCA differs across repeated measures conditions?
Just one (maybe silly) idea. Save 1st principal component scores variable for condition A (PC1A) and 1st principal component scores variable for condition B (PC1B). The scores should be "raw", that is, their varia... | How to test whether variance explained by first factor of PCA differs across repeated measures condi
Just one (maybe silly) idea. Save 1st principal component scores variable for condition A (PC1A) and 1st principal component scores variable for condition B (PC1B). The scores should be "raw", that is |
37,455 | How to test whether variance explained by first factor of PCA differs across repeated measures conditions? | Did I get your answer right? - You want to test if there is statistically significant difference between the two conditions?
Perhabs vegan::adonis() is something for you? Don´t know if that´s what your looking for.
It works on the distance-matrix and compares distances within a condition are bigger then between conditi... | How to test whether variance explained by first factor of PCA differs across repeated measures condi | Did I get your answer right? - You want to test if there is statistically significant difference between the two conditions?
Perhabs vegan::adonis() is something for you? Don´t know if that´s what you | How to test whether variance explained by first factor of PCA differs across repeated measures conditions?
Did I get your answer right? - You want to test if there is statistically significant difference between the two conditions?
Perhabs vegan::adonis() is something for you? Don´t know if that´s what your looking for... | How to test whether variance explained by first factor of PCA differs across repeated measures condi
Did I get your answer right? - You want to test if there is statistically significant difference between the two conditions?
Perhabs vegan::adonis() is something for you? Don´t know if that´s what you |
37,456 | How to test whether variance explained by first factor of PCA differs across repeated measures conditions? | Permutation test
To test the null hypothesis directly, use a permutation test.
Let the first PC in condition $A$ explain $a<100\%$ of variance, and the first PC in condition $B$ explain $b<100\%$ of variance. Your hypothesis is that $b>a$, so we can define $c=b-a$ as the statistic of interest, and the hypothesis is th... | How to test whether variance explained by first factor of PCA differs across repeated measures condi | Permutation test
To test the null hypothesis directly, use a permutation test.
Let the first PC in condition $A$ explain $a<100\%$ of variance, and the first PC in condition $B$ explain $b<100\%$ of | How to test whether variance explained by first factor of PCA differs across repeated measures conditions?
Permutation test
To test the null hypothesis directly, use a permutation test.
Let the first PC in condition $A$ explain $a<100\%$ of variance, and the first PC in condition $B$ explain $b<100\%$ of variance. You... | How to test whether variance explained by first factor of PCA differs across repeated measures condi
Permutation test
To test the null hypothesis directly, use a permutation test.
Let the first PC in condition $A$ explain $a<100\%$ of variance, and the first PC in condition $B$ explain $b<100\%$ of |
37,457 | How to test whether variance explained by first factor of PCA differs across repeated measures conditions? | This is only an outline of idea. The proportion of variance is defined as
$$\frac{\lambda_1}{\lambda_1+...+\lambda_6},$$
where $\lambda_i$ are the eigenvalues of covariance matrix. Now if we use instead the eigenvalues of correlation matrix then $\lambda_1+...+\lambda_6=6$, since the sum of eigenvalues of a matrix is e... | How to test whether variance explained by first factor of PCA differs across repeated measures condi | This is only an outline of idea. The proportion of variance is defined as
$$\frac{\lambda_1}{\lambda_1+...+\lambda_6},$$
where $\lambda_i$ are the eigenvalues of covariance matrix. Now if we use inste | How to test whether variance explained by first factor of PCA differs across repeated measures conditions?
This is only an outline of idea. The proportion of variance is defined as
$$\frac{\lambda_1}{\lambda_1+...+\lambda_6},$$
where $\lambda_i$ are the eigenvalues of covariance matrix. Now if we use instead the eigenv... | How to test whether variance explained by first factor of PCA differs across repeated measures condi
This is only an outline of idea. The proportion of variance is defined as
$$\frac{\lambda_1}{\lambda_1+...+\lambda_6},$$
where $\lambda_i$ are the eigenvalues of covariance matrix. Now if we use inste |
37,458 | Identifying sequential patterns | You can map the data into a feature space where sequence is important, along with both statistics calculated over sliding windows & cumulative statistics, and use that in a decision tree.
A decision tree could handle both sequences and non-sequential data. This may substantially reduce your data complexity. | Identifying sequential patterns | You can map the data into a feature space where sequence is important, along with both statistics calculated over sliding windows & cumulative statistics, and use that in a decision tree.
A decision t | Identifying sequential patterns
You can map the data into a feature space where sequence is important, along with both statistics calculated over sliding windows & cumulative statistics, and use that in a decision tree.
A decision tree could handle both sequences and non-sequential data. This may substantially reduce ... | Identifying sequential patterns
You can map the data into a feature space where sequence is important, along with both statistics calculated over sliding windows & cumulative statistics, and use that in a decision tree.
A decision t |
37,459 | Identifying sequential patterns | You may try other sequential pattern mining algorithm.
For example, the open-source SPMF java data mining library offers SPADE, but also PrefixSpan, SPAM, CM-SPAM, CM-SPADE, GSP, etc (by the way, I'm the project founder). To my knowledge CM-SPADE usually is faster than SPADE. In terms of memory perhaps that SPAM use... | Identifying sequential patterns | You may try other sequential pattern mining algorithm.
For example, the open-source SPMF java data mining library offers SPADE, but also PrefixSpan, SPAM, CM-SPAM, CM-SPADE, GSP, etc (by the way, I'm | Identifying sequential patterns
You may try other sequential pattern mining algorithm.
For example, the open-source SPMF java data mining library offers SPADE, but also PrefixSpan, SPAM, CM-SPAM, CM-SPADE, GSP, etc (by the way, I'm the project founder). To my knowledge CM-SPADE usually is faster than SPADE. In terms... | Identifying sequential patterns
You may try other sequential pattern mining algorithm.
For example, the open-source SPMF java data mining library offers SPADE, but also PrefixSpan, SPAM, CM-SPAM, CM-SPADE, GSP, etc (by the way, I'm |
37,460 | What is a vector autoregressive model? | If purely from managerial perspective, VAR is practically the same as linear regression. The main difference is that in VAR you have several dependent variables instead of one. This means that instead of one linear regression you have several. Your interpretation of linear regression remains valid, since each VAR regre... | What is a vector autoregressive model? | If purely from managerial perspective, VAR is practically the same as linear regression. The main difference is that in VAR you have several dependent variables instead of one. This means that instead | What is a vector autoregressive model?
If purely from managerial perspective, VAR is practically the same as linear regression. The main difference is that in VAR you have several dependent variables instead of one. This means that instead of one linear regression you have several. Your interpretation of linear regress... | What is a vector autoregressive model?
If purely from managerial perspective, VAR is practically the same as linear regression. The main difference is that in VAR you have several dependent variables instead of one. This means that instead |
37,461 | What is a vector autoregressive model? | In Var models instead of using several dependent variable we use several independent variable and their effect on one dependent variable. | What is a vector autoregressive model? | In Var models instead of using several dependent variable we use several independent variable and their effect on one dependent variable. | What is a vector autoregressive model?
In Var models instead of using several dependent variable we use several independent variable and their effect on one dependent variable. | What is a vector autoregressive model?
In Var models instead of using several dependent variable we use several independent variable and their effect on one dependent variable. |
37,462 | How to visualize a GraphML multitree? | Have you tried GraphViz? Here is a tutorial and using GraphML. | How to visualize a GraphML multitree? | Have you tried GraphViz? Here is a tutorial and using GraphML. | How to visualize a GraphML multitree?
Have you tried GraphViz? Here is a tutorial and using GraphML. | How to visualize a GraphML multitree?
Have you tried GraphViz? Here is a tutorial and using GraphML. |
37,463 | Calibrated boosted decision trees in R or MATLAB | About R, I would vote for the gbm package; there's a vignette that provides a good overview: Generalized Boosted Models: A guide to the gbm package. If you are looking for an unified interface to ML algorithms, I recommend the caret package which has built-in facilities for data preprocessing, resampling, and comparati... | Calibrated boosted decision trees in R or MATLAB | About R, I would vote for the gbm package; there's a vignette that provides a good overview: Generalized Boosted Models: A guide to the gbm package. If you are looking for an unified interface to ML a | Calibrated boosted decision trees in R or MATLAB
About R, I would vote for the gbm package; there's a vignette that provides a good overview: Generalized Boosted Models: A guide to the gbm package. If you are looking for an unified interface to ML algorithms, I recommend the caret package which has built-in facilities ... | Calibrated boosted decision trees in R or MATLAB
About R, I would vote for the gbm package; there's a vignette that provides a good overview: Generalized Boosted Models: A guide to the gbm package. If you are looking for an unified interface to ML a |
37,464 | How can I estimate the time at which 50% of a binomial variable will have transitioned? | As became evident in comments to the question, the data consist of only four observations of time to bud burst. (It would be a mistake to analyze them as if they were 16 independent values.) They consist of intervals of times rather than exact times:
[1,8], [8,16], [16,24], [24,32]
There are several approaches one m... | How can I estimate the time at which 50% of a binomial variable will have transitioned? | As became evident in comments to the question, the data consist of only four observations of time to bud burst. (It would be a mistake to analyze them as if they were 16 independent values.) They co | How can I estimate the time at which 50% of a binomial variable will have transitioned?
As became evident in comments to the question, the data consist of only four observations of time to bud burst. (It would be a mistake to analyze them as if they were 16 independent values.) They consist of intervals of times rath... | How can I estimate the time at which 50% of a binomial variable will have transitioned?
As became evident in comments to the question, the data consist of only four observations of time to bud burst. (It would be a mistake to analyze them as if they were 16 independent values.) They co |
37,465 | How can I estimate the time at which 50% of a binomial variable will have transitioned? | Here is a simple approach that does not use logistic regression, but does attempt to use the suggestions above. Calculation of summary stats assumes, perhaps naively, that the date is normally distributed.
Please pardon inelegant code
write a function to estimate the day of budbreak for each individual: use the day o... | How can I estimate the time at which 50% of a binomial variable will have transitioned? | Here is a simple approach that does not use logistic regression, but does attempt to use the suggestions above. Calculation of summary stats assumes, perhaps naively, that the date is normally distrib | How can I estimate the time at which 50% of a binomial variable will have transitioned?
Here is a simple approach that does not use logistic regression, but does attempt to use the suggestions above. Calculation of summary stats assumes, perhaps naively, that the date is normally distributed.
Please pardon inelegant c... | How can I estimate the time at which 50% of a binomial variable will have transitioned?
Here is a simple approach that does not use logistic regression, but does attempt to use the suggestions above. Calculation of summary stats assumes, perhaps naively, that the date is normally distrib |
37,466 | How can I estimate the time at which 50% of a binomial variable will have transitioned? | We know that the $t_1$ transition time (from state 0 to state 1) of subject id=1 was between two boundaries: $24<t_1<32$. An approximation is to assume that $t_1$ may have taken values within this range with uniform probability. Resampling the $t_i$ values we can get an approximate distribution of $\text{median}(t_i)$:... | How can I estimate the time at which 50% of a binomial variable will have transitioned? | We know that the $t_1$ transition time (from state 0 to state 1) of subject id=1 was between two boundaries: $24<t_1<32$. An approximation is to assume that $t_1$ may have taken values within this ran | How can I estimate the time at which 50% of a binomial variable will have transitioned?
We know that the $t_1$ transition time (from state 0 to state 1) of subject id=1 was between two boundaries: $24<t_1<32$. An approximation is to assume that $t_1$ may have taken values within this range with uniform probability. Res... | How can I estimate the time at which 50% of a binomial variable will have transitioned?
We know that the $t_1$ transition time (from state 0 to state 1) of subject id=1 was between two boundaries: $24<t_1<32$. An approximation is to assume that $t_1$ may have taken values within this ran |
37,467 | How can I estimate the time at which 50% of a binomial variable will have transitioned? | You could use discrete time hazard model fit with logistic regression (using a person period data set). See Applied Longitudinal Data Analysis - software
and Book Chapters 10-12.
Allison also discusses
You data set is tiny though. | How can I estimate the time at which 50% of a binomial variable will have transitioned? | You could use discrete time hazard model fit with logistic regression (using a person period data set). See Applied Longitudinal Data Analysis - software
and Book Chapters 10-12.
Allison also discuss | How can I estimate the time at which 50% of a binomial variable will have transitioned?
You could use discrete time hazard model fit with logistic regression (using a person period data set). See Applied Longitudinal Data Analysis - software
and Book Chapters 10-12.
Allison also discusses
You data set is tiny though. | How can I estimate the time at which 50% of a binomial variable will have transitioned?
You could use discrete time hazard model fit with logistic regression (using a person period data set). See Applied Longitudinal Data Analysis - software
and Book Chapters 10-12.
Allison also discuss |
37,468 | How can I estimate the time at which 50% of a binomial variable will have transitioned? | Assuming that you will have more data of the same structure you will be able to use the actuarial (life table) method to estimate median survival. | How can I estimate the time at which 50% of a binomial variable will have transitioned? | Assuming that you will have more data of the same structure you will be able to use the actuarial (life table) method to estimate median survival. | How can I estimate the time at which 50% of a binomial variable will have transitioned?
Assuming that you will have more data of the same structure you will be able to use the actuarial (life table) method to estimate median survival. | How can I estimate the time at which 50% of a binomial variable will have transitioned?
Assuming that you will have more data of the same structure you will be able to use the actuarial (life table) method to estimate median survival. |
37,469 | Introductory statistics video courses for social scientists | http://www.statisticslectures.com/ - I think, this is a really high quality resource. Explanations are clear, concise and very informative. | Introductory statistics video courses for social scientists | http://www.statisticslectures.com/ - I think, this is a really high quality resource. Explanations are clear, concise and very informative. | Introductory statistics video courses for social scientists
http://www.statisticslectures.com/ - I think, this is a really high quality resource. Explanations are clear, concise and very informative. | Introductory statistics video courses for social scientists
http://www.statisticslectures.com/ - I think, this is a really high quality resource. Explanations are clear, concise and very informative. |
37,470 | Introductory statistics video courses for social scientists | The Udacity course :
*Introduction to Statistics (st101)
Making Decisions Based on Data *
Was really good. I learned a lot and it's focused on real world examples. They have an introductory video here :
http://www.udacity.com/overview/Course/st101/CourseRev/1 | Introductory statistics video courses for social scientists | The Udacity course :
*Introduction to Statistics (st101)
Making Decisions Based on Data *
Was really good. I learned a lot and it's focused on real world examples. They have an introductory video her | Introductory statistics video courses for social scientists
The Udacity course :
*Introduction to Statistics (st101)
Making Decisions Based on Data *
Was really good. I learned a lot and it's focused on real world examples. They have an introductory video here :
http://www.udacity.com/overview/Course/st101/CourseRev/... | Introductory statistics video courses for social scientists
The Udacity course :
*Introduction to Statistics (st101)
Making Decisions Based on Data *
Was really good. I learned a lot and it's focused on real world examples. They have an introductory video her |
37,471 | Multiple regression with small data sets | As you want to select a few predictors from your data set, I would suggest a simple linear regression with $L_1$ penalty or using the LASSO (penalized linear regression). Your case is suited for regression with LASSO penalty as your sample size, $n = 50$, and the number of predictors, $p=30$. Changing the tuning parame... | Multiple regression with small data sets | As you want to select a few predictors from your data set, I would suggest a simple linear regression with $L_1$ penalty or using the LASSO (penalized linear regression). Your case is suited for regre | Multiple regression with small data sets
As you want to select a few predictors from your data set, I would suggest a simple linear regression with $L_1$ penalty or using the LASSO (penalized linear regression). Your case is suited for regression with LASSO penalty as your sample size, $n = 50$, and the number of predi... | Multiple regression with small data sets
As you want to select a few predictors from your data set, I would suggest a simple linear regression with $L_1$ penalty or using the LASSO (penalized linear regression). Your case is suited for regre |
37,472 | Statistical test for trend (continuous variable) in Stata or R | It seems that your problem can be stated as change-point problem. R packages dealing with such type of problems are segmented and strucchange. Since you want to look into changes in time trend (and time trends always need special treatment in linear regression), I suggest differencing your hemoglobin level data and the... | Statistical test for trend (continuous variable) in Stata or R | It seems that your problem can be stated as change-point problem. R packages dealing with such type of problems are segmented and strucchange. Since you want to look into changes in time trend (and ti | Statistical test for trend (continuous variable) in Stata or R
It seems that your problem can be stated as change-point problem. R packages dealing with such type of problems are segmented and strucchange. Since you want to look into changes in time trend (and time trends always need special treatment in linear regress... | Statistical test for trend (continuous variable) in Stata or R
It seems that your problem can be stated as change-point problem. R packages dealing with such type of problems are segmented and strucchange. Since you want to look into changes in time trend (and ti |
37,473 | Two-sample permutation Kolmogorov-Smirnov tests | The answer depends on the nature of the data generation process and on the alternative hypothesis you have in mind.
Your test is a kind of unweighted chi-square. Because of this lack of weighting, changes that principally affect the less-populated categories will be difficult to detect. For example, your test is goin... | Two-sample permutation Kolmogorov-Smirnov tests | The answer depends on the nature of the data generation process and on the alternative hypothesis you have in mind.
Your test is a kind of unweighted chi-square. Because of this lack of weighting, ch | Two-sample permutation Kolmogorov-Smirnov tests
The answer depends on the nature of the data generation process and on the alternative hypothesis you have in mind.
Your test is a kind of unweighted chi-square. Because of this lack of weighting, changes that principally affect the less-populated categories will be diff... | Two-sample permutation Kolmogorov-Smirnov tests
The answer depends on the nature of the data generation process and on the alternative hypothesis you have in mind.
Your test is a kind of unweighted chi-square. Because of this lack of weighting, ch |
37,474 | Determination of effect size for a repeated measures ANOVA power analysis | Assuming you are going to average the first 12 months to form a baseline measure and the second 12 months to form as a follow-up measure, your problem reduces to a repeated measures t-test.
G*Power
You might want to check out the following menu in G*Power 3:
Tests - Means - Two Dependent Groups (matched pairs).
Use A ... | Determination of effect size for a repeated measures ANOVA power analysis | Assuming you are going to average the first 12 months to form a baseline measure and the second 12 months to form as a follow-up measure, your problem reduces to a repeated measures t-test.
G*Power
Yo | Determination of effect size for a repeated measures ANOVA power analysis
Assuming you are going to average the first 12 months to form a baseline measure and the second 12 months to form as a follow-up measure, your problem reduces to a repeated measures t-test.
G*Power
You might want to check out the following menu i... | Determination of effect size for a repeated measures ANOVA power analysis
Assuming you are going to average the first 12 months to form a baseline measure and the second 12 months to form as a follow-up measure, your problem reduces to a repeated measures t-test.
G*Power
Yo |
37,475 | Estimating parameters of sum-stable RV via L-estimators | The Levy distribution has 4 parameter. Each of them has a quantile-based sample equivalent:
$\mu$, the location parameter, can be estimated by the median. This is a high efficiency alternative (ARE$\approx 0.85$).
$\gamma$, the scale parameter, can be estimated by the median absolute deviation (or more efficiently yet... | Estimating parameters of sum-stable RV via L-estimators | The Levy distribution has 4 parameter. Each of them has a quantile-based sample equivalent:
$\mu$, the location parameter, can be estimated by the median. This is a high efficiency alternative (ARE$\ | Estimating parameters of sum-stable RV via L-estimators
The Levy distribution has 4 parameter. Each of them has a quantile-based sample equivalent:
$\mu$, the location parameter, can be estimated by the median. This is a high efficiency alternative (ARE$\approx 0.85$).
$\gamma$, the scale parameter, can be estimated b... | Estimating parameters of sum-stable RV via L-estimators
The Levy distribution has 4 parameter. Each of them has a quantile-based sample equivalent:
$\mu$, the location parameter, can be estimated by the median. This is a high efficiency alternative (ARE$\ |
37,476 | If the curse of dimensionality exists, how does embedding search work? | The origion of vector space model is as follows:
The idea that the meaning of a word might be modeled as a point in a multi-
dimensional semantic space came from psychologists like Charles E. Osgood, who had been studying how people responded to the meaning of words by assigning val- ues along scales like happy/sad or... | If the curse of dimensionality exists, how does embedding search work? | The origion of vector space model is as follows:
The idea that the meaning of a word might be modeled as a point in a multi-
dimensional semantic space came from psychologists like Charles E. Osgood, | If the curse of dimensionality exists, how does embedding search work?
The origion of vector space model is as follows:
The idea that the meaning of a word might be modeled as a point in a multi-
dimensional semantic space came from psychologists like Charles E. Osgood, who had been studying how people responded to th... | If the curse of dimensionality exists, how does embedding search work?
The origion of vector space model is as follows:
The idea that the meaning of a word might be modeled as a point in a multi-
dimensional semantic space came from psychologists like Charles E. Osgood, |
37,477 | Can anyone point me towards tutorials describing how to use the Kalman filter for forecasting? | Unfortunately, the Kalman filter methodology is a fairly advanced topic in econometrics, hence it is quite difficult to find simple examples, both because it is a complex topic that requires a deeper knowledge of the maths behind it, and because it is easier to make tutorials of simple things.
However, here you can fin... | Can anyone point me towards tutorials describing how to use the Kalman filter for forecasting? | Unfortunately, the Kalman filter methodology is a fairly advanced topic in econometrics, hence it is quite difficult to find simple examples, both because it is a complex topic that requires a deeper | Can anyone point me towards tutorials describing how to use the Kalman filter for forecasting?
Unfortunately, the Kalman filter methodology is a fairly advanced topic in econometrics, hence it is quite difficult to find simple examples, both because it is a complex topic that requires a deeper knowledge of the maths be... | Can anyone point me towards tutorials describing how to use the Kalman filter for forecasting?
Unfortunately, the Kalman filter methodology is a fairly advanced topic in econometrics, hence it is quite difficult to find simple examples, both because it is a complex topic that requires a deeper |
37,478 | Can anyone point me towards tutorials describing how to use the Kalman filter for forecasting? | The best I found till now is this open source interactive book: Kalman and Bayesian Filters in Python
Hope this helps ! | Can anyone point me towards tutorials describing how to use the Kalman filter for forecasting? | The best I found till now is this open source interactive book: Kalman and Bayesian Filters in Python
Hope this helps ! | Can anyone point me towards tutorials describing how to use the Kalman filter for forecasting?
The best I found till now is this open source interactive book: Kalman and Bayesian Filters in Python
Hope this helps ! | Can anyone point me towards tutorials describing how to use the Kalman filter for forecasting?
The best I found till now is this open source interactive book: Kalman and Bayesian Filters in Python
Hope this helps ! |
37,479 | Intuitive understanding of instrumental variables for natural experiments | I interpret the background to be, you are interested in evaluating the effect of some (endogenous) "treatment" on some "outcome," and we assume that there is a valid "instrumental variable" for "treatment."
so should I think intuitively as if it is to some degree a random experiment on a subset of units?
Basically, y... | Intuitive understanding of instrumental variables for natural experiments | I interpret the background to be, you are interested in evaluating the effect of some (endogenous) "treatment" on some "outcome," and we assume that there is a valid "instrumental variable" for "treat | Intuitive understanding of instrumental variables for natural experiments
I interpret the background to be, you are interested in evaluating the effect of some (endogenous) "treatment" on some "outcome," and we assume that there is a valid "instrumental variable" for "treatment."
so should I think intuitively as if it... | Intuitive understanding of instrumental variables for natural experiments
I interpret the background to be, you are interested in evaluating the effect of some (endogenous) "treatment" on some "outcome," and we assume that there is a valid "instrumental variable" for "treat |
37,480 | Error Bars for Histogram with Uncertain Data | I thought about it some more, and I have a couple of ideas.
(1) About measurement uncertainty: from what you said, it's big enough to take into account. I agree with the formula for qi -- this is just the mass of the distribution for x[i] which falls into B[k]. From that, it looks to me that the mean of the proportion ... | Error Bars for Histogram with Uncertain Data | I thought about it some more, and I have a couple of ideas.
(1) About measurement uncertainty: from what you said, it's big enough to take into account. I agree with the formula for qi -- this is just | Error Bars for Histogram with Uncertain Data
I thought about it some more, and I have a couple of ideas.
(1) About measurement uncertainty: from what you said, it's big enough to take into account. I agree with the formula for qi -- this is just the mass of the distribution for x[i] which falls into B[k]. From that, it... | Error Bars for Histogram with Uncertain Data
I thought about it some more, and I have a couple of ideas.
(1) About measurement uncertainty: from what you said, it's big enough to take into account. I agree with the formula for qi -- this is just |
37,481 | Error Bars for Histogram with Uncertain Data | I have a similar problem and I have a solution in mind, though it's more complicated than I'd like, which is how I stumbled upon this answer: seeking an easier answer.
With that preamble out of the way, I'll share the solution I had in mind. It is more complicated than I'd like. And I am sorry that this is coming ove... | Error Bars for Histogram with Uncertain Data | I have a similar problem and I have a solution in mind, though it's more complicated than I'd like, which is how I stumbled upon this answer: seeking an easier answer.
With that preamble out of the wa | Error Bars for Histogram with Uncertain Data
I have a similar problem and I have a solution in mind, though it's more complicated than I'd like, which is how I stumbled upon this answer: seeking an easier answer.
With that preamble out of the way, I'll share the solution I had in mind. It is more complicated than I'd ... | Error Bars for Histogram with Uncertain Data
I have a similar problem and I have a solution in mind, though it's more complicated than I'd like, which is how I stumbled upon this answer: seeking an easier answer.
With that preamble out of the wa |
37,482 | Error Bars for Histogram with Uncertain Data | Check this link about the error bars for cases similar to the one you mention.
https://www.science20.com/quantum_diaries_survivor/those_deceiving_error_bars-85735
We are measuring the number counts within bins. (Similar to building a histogram).
What is usually done? R- The errors in each bin are considered symmetric, ... | Error Bars for Histogram with Uncertain Data | Check this link about the error bars for cases similar to the one you mention.
https://www.science20.com/quantum_diaries_survivor/those_deceiving_error_bars-85735
We are measuring the number counts wi | Error Bars for Histogram with Uncertain Data
Check this link about the error bars for cases similar to the one you mention.
https://www.science20.com/quantum_diaries_survivor/those_deceiving_error_bars-85735
We are measuring the number counts within bins. (Similar to building a histogram).
What is usually done? R- The ... | Error Bars for Histogram with Uncertain Data
Check this link about the error bars for cases similar to the one you mention.
https://www.science20.com/quantum_diaries_survivor/those_deceiving_error_bars-85735
We are measuring the number counts wi |
37,483 | expected value of a fishing strategy | Following whuber's suggestion, let's look at a simple example. Say we need to catch 2 fish and we are given 4 days. To begin we denote the two fish we have on day two $x_1$ and $x_2$, and the minimum denoted by $x$. On the third day, I release the smallest fish and re-catch if the following holds
\begin{align}
(1-x^2)\... | expected value of a fishing strategy | Following whuber's suggestion, let's look at a simple example. Say we need to catch 2 fish and we are given 4 days. To begin we denote the two fish we have on day two $x_1$ and $x_2$, and the minimum | expected value of a fishing strategy
Following whuber's suggestion, let's look at a simple example. Say we need to catch 2 fish and we are given 4 days. To begin we denote the two fish we have on day two $x_1$ and $x_2$, and the minimum denoted by $x$. On the third day, I release the smallest fish and re-catch if the f... | expected value of a fishing strategy
Following whuber's suggestion, let's look at a simple example. Say we need to catch 2 fish and we are given 4 days. To begin we denote the two fish we have on day two $x_1$ and $x_2$, and the minimum |
37,484 | Why BERT use learned positional embedding? | Here is my current understanding to my own question.
It probably related BERT's transfer learning background. The learned-lookup-table indeed increase learning effort in pretrain stage, but the extra effort can be almost ingnored compared to number of the trainable parameters in transformer encoder, it also should be a... | Why BERT use learned positional embedding? | Here is my current understanding to my own question.
It probably related BERT's transfer learning background. The learned-lookup-table indeed increase learning effort in pretrain stage, but the extra | Why BERT use learned positional embedding?
Here is my current understanding to my own question.
It probably related BERT's transfer learning background. The learned-lookup-table indeed increase learning effort in pretrain stage, but the extra effort can be almost ingnored compared to number of the trainable parameters ... | Why BERT use learned positional embedding?
Here is my current understanding to my own question.
It probably related BERT's transfer learning background. The learned-lookup-table indeed increase learning effort in pretrain stage, but the extra |
37,485 | Why BERT use learned positional embedding? | Fixed length
BERT, same as Transformer, use attention as a key feature. The attention as used in those models, has a fixed span as well.
Cannot reflect relative distance
We assume neural networks to be universal function approximators. If that is the case, why wouldn't it be able to learn building the Fourier ter... | Why BERT use learned positional embedding? | Fixed length
BERT, same as Transformer, use attention as a key feature. The attention as used in those models, has a fixed span as well.
Cannot reflect relative distance
We assume neural network | Why BERT use learned positional embedding?
Fixed length
BERT, same as Transformer, use attention as a key feature. The attention as used in those models, has a fixed span as well.
Cannot reflect relative distance
We assume neural networks to be universal function approximators. If that is the case, why wouldn't i... | Why BERT use learned positional embedding?
Fixed length
BERT, same as Transformer, use attention as a key feature. The attention as used in those models, has a fixed span as well.
Cannot reflect relative distance
We assume neural network |
37,486 | Why are there large discrepancies between Wald and bootstrapped confidence intervals for parameters of a lmer model in R? | Basically, the Wald statistic is not good and you shouldn't trust it for mixed models. It uses a much cruder approximation to the actual likelihood than you get with the profile and boot.ci methods. If R (and SAS and JMP and...) would have been written today, they would not have bothered implementing Wald stats. That's... | Why are there large discrepancies between Wald and bootstrapped confidence intervals for parameters | Basically, the Wald statistic is not good and you shouldn't trust it for mixed models. It uses a much cruder approximation to the actual likelihood than you get with the profile and boot.ci methods. I | Why are there large discrepancies between Wald and bootstrapped confidence intervals for parameters of a lmer model in R?
Basically, the Wald statistic is not good and you shouldn't trust it for mixed models. It uses a much cruder approximation to the actual likelihood than you get with the profile and boot.ci methods.... | Why are there large discrepancies between Wald and bootstrapped confidence intervals for parameters
Basically, the Wald statistic is not good and you shouldn't trust it for mixed models. It uses a much cruder approximation to the actual likelihood than you get with the profile and boot.ci methods. I |
37,487 | Are min$(X_1,\ldots,X_n)$ and min$(X_1Y_1,\ldots,X_nY_n)$ independent for $n$ to infinity? | Just a quick thought of mine:
The condition that the product $X_i Y_i$ is smaller than $X_2$ removes all possibilities of $X_i$ being larger than $(X2_/b_n)+a_n$.
EDIT:
Yes you understood me correctly! Imagine the conditional probability of both (the product and Xi alone) being small enough: P(Xi|XiYi)! Given the prod... | Are min$(X_1,\ldots,X_n)$ and min$(X_1Y_1,\ldots,X_nY_n)$ independent for $n$ to infinity? | Just a quick thought of mine:
The condition that the product $X_i Y_i$ is smaller than $X_2$ removes all possibilities of $X_i$ being larger than $(X2_/b_n)+a_n$.
EDIT:
Yes you understood me correctl | Are min$(X_1,\ldots,X_n)$ and min$(X_1Y_1,\ldots,X_nY_n)$ independent for $n$ to infinity?
Just a quick thought of mine:
The condition that the product $X_i Y_i$ is smaller than $X_2$ removes all possibilities of $X_i$ being larger than $(X2_/b_n)+a_n$.
EDIT:
Yes you understood me correctly! Imagine the conditional pr... | Are min$(X_1,\ldots,X_n)$ and min$(X_1Y_1,\ldots,X_nY_n)$ independent for $n$ to infinity?
Just a quick thought of mine:
The condition that the product $X_i Y_i$ is smaller than $X_2$ removes all possibilities of $X_i$ being larger than $(X2_/b_n)+a_n$.
EDIT:
Yes you understood me correctl |
37,488 | Difference between eligibility traces and momentum? | For simplicity, let us consider the case of no discount ($\gamma = 1$). This setting is sufficient to understand the difference between eligibility trace and momentum.
Then, $\mathbf{z}_t$ appeared in eligibility trace is the sum of the history of the gradients
$\nabla \hat{v}_t$:
$$
\mathbf{z}_t = \sum_{k=0}^t \nabl... | Difference between eligibility traces and momentum? | For simplicity, let us consider the case of no discount ($\gamma = 1$). This setting is sufficient to understand the difference between eligibility trace and momentum.
Then, $\mathbf{z}_t$ appeared in | Difference between eligibility traces and momentum?
For simplicity, let us consider the case of no discount ($\gamma = 1$). This setting is sufficient to understand the difference between eligibility trace and momentum.
Then, $\mathbf{z}_t$ appeared in eligibility trace is the sum of the history of the gradients
$\nab... | Difference between eligibility traces and momentum?
For simplicity, let us consider the case of no discount ($\gamma = 1$). This setting is sufficient to understand the difference between eligibility trace and momentum.
Then, $\mathbf{z}_t$ appeared in |
37,489 | Maximum Likelihood Estimator of $P(Y_1=1)$ where $Y_i=1$ if $X_i>0$ and $0$ otherwise, given $X_1,\dots,X_n\sim N(\theta,1)$ | First, denoting $\Phi$ to be the standard normal c.d.f., we have \begin{align*}\psi &= P(Y_1=1) \\ &= P(X_1>0) \\ &= 1-P(X_1 \le 0) \\ &= 1-P(X_1-\theta\le -\theta)\\ &=1-\Phi(-\theta)\\ &=\Phi(\theta),\end{align*} where in the third line, we use the fact that $X_1-\theta\sim N(0,1)$. Consequently, by equivariance of t... | Maximum Likelihood Estimator of $P(Y_1=1)$ where $Y_i=1$ if $X_i>0$ and $0$ otherwise, given $X_1,\d | First, denoting $\Phi$ to be the standard normal c.d.f., we have \begin{align*}\psi &= P(Y_1=1) \\ &= P(X_1>0) \\ &= 1-P(X_1 \le 0) \\ &= 1-P(X_1-\theta\le -\theta)\\ &=1-\Phi(-\theta)\\ &=\Phi(\theta | Maximum Likelihood Estimator of $P(Y_1=1)$ where $Y_i=1$ if $X_i>0$ and $0$ otherwise, given $X_1,\dots,X_n\sim N(\theta,1)$
First, denoting $\Phi$ to be the standard normal c.d.f., we have \begin{align*}\psi &= P(Y_1=1) \\ &= P(X_1>0) \\ &= 1-P(X_1 \le 0) \\ &= 1-P(X_1-\theta\le -\theta)\\ &=1-\Phi(-\theta)\\ &=\Phi(\... | Maximum Likelihood Estimator of $P(Y_1=1)$ where $Y_i=1$ if $X_i>0$ and $0$ otherwise, given $X_1,\d
First, denoting $\Phi$ to be the standard normal c.d.f., we have \begin{align*}\psi &= P(Y_1=1) \\ &= P(X_1>0) \\ &= 1-P(X_1 \le 0) \\ &= 1-P(X_1-\theta\le -\theta)\\ &=1-\Phi(-\theta)\\ &=\Phi(\theta |
37,490 | Time complexity of simple linear regression in $L_1$ norm | There is a $O(n^2)$ running time algorithm.
It is fairly easy to derive: There exists an optimal line that contains one of the given points (in fact, at least 2 points). There exists a $O(n)$ time algorithm to decide the best line that goes through a given point. Basically a weighted median computation. Together, it im... | Time complexity of simple linear regression in $L_1$ norm | There is a $O(n^2)$ running time algorithm.
It is fairly easy to derive: There exists an optimal line that contains one of the given points (in fact, at least 2 points). There exists a $O(n)$ time alg | Time complexity of simple linear regression in $L_1$ norm
There is a $O(n^2)$ running time algorithm.
It is fairly easy to derive: There exists an optimal line that contains one of the given points (in fact, at least 2 points). There exists a $O(n)$ time algorithm to decide the best line that goes through a given point... | Time complexity of simple linear regression in $L_1$ norm
There is a $O(n^2)$ running time algorithm.
It is fairly easy to derive: There exists an optimal line that contains one of the given points (in fact, at least 2 points). There exists a $O(n)$ time alg |
37,491 | Alternatives to Journal of Statistical Software? | You can try JOSS: Journal of Open Source Software, that is an academic journal (ISSN 2475-9066) with a formal peer review process that is designed to improve the quality of the software submitted.
They create a DOI after publication, follow the Covenant code of conduct and are affiliated of the Open Source Initiative. | Alternatives to Journal of Statistical Software? | You can try JOSS: Journal of Open Source Software, that is an academic journal (ISSN 2475-9066) with a formal peer review process that is designed to improve the quality of the software submitted.
Th | Alternatives to Journal of Statistical Software?
You can try JOSS: Journal of Open Source Software, that is an academic journal (ISSN 2475-9066) with a formal peer review process that is designed to improve the quality of the software submitted.
They create a DOI after publication, follow the Covenant code of conduct ... | Alternatives to Journal of Statistical Software?
You can try JOSS: Journal of Open Source Software, that is an academic journal (ISSN 2475-9066) with a formal peer review process that is designed to improve the quality of the software submitted.
Th |
37,492 | How to decide moving window size for time series prediction? | I would say that your first approach seems like a good start, it seems better to me than your second one. Your assessment of the possible risks, are correct as you could interpret this as tweaking hyperparameters on the test set come with the risk with a performance estimate is too optimistic.
It could be an idea to t... | How to decide moving window size for time series prediction? | I would say that your first approach seems like a good start, it seems better to me than your second one. Your assessment of the possible risks, are correct as you could interpret this as tweaking hyp | How to decide moving window size for time series prediction?
I would say that your first approach seems like a good start, it seems better to me than your second one. Your assessment of the possible risks, are correct as you could interpret this as tweaking hyperparameters on the test set come with the risk with a perf... | How to decide moving window size for time series prediction?
I would say that your first approach seems like a good start, it seems better to me than your second one. Your assessment of the possible risks, are correct as you could interpret this as tweaking hyp |
37,493 | Sparse linear regression 0-norm and 1-norm | If features are correlated, you should use elastic net and not lasso.
Roughly, if two features are correlated, lasso would choose the feature $i$ over $j$ if it has the better reward on the loss function, this means a smaller absolute value $|\beta_i|$ of the regression coefficient together with a good decrease in the ... | Sparse linear regression 0-norm and 1-norm | If features are correlated, you should use elastic net and not lasso.
Roughly, if two features are correlated, lasso would choose the feature $i$ over $j$ if it has the better reward on the loss funct | Sparse linear regression 0-norm and 1-norm
If features are correlated, you should use elastic net and not lasso.
Roughly, if two features are correlated, lasso would choose the feature $i$ over $j$ if it has the better reward on the loss function, this means a smaller absolute value $|\beta_i|$ of the regression coeffi... | Sparse linear regression 0-norm and 1-norm
If features are correlated, you should use elastic net and not lasso.
Roughly, if two features are correlated, lasso would choose the feature $i$ over $j$ if it has the better reward on the loss funct |
37,494 | Coding resources: Accessible introductions to Bayesian Structural Time series? | I would say Stan (disclosure: I am one of the Stan developers). For most of the models you mention, there is not going to be a Gibbs sampler with known full-conditional distributions for all of the parameters and even for the exceptions, the chains might not mix well under Gibbs sampling.
That said, here are some links... | Coding resources: Accessible introductions to Bayesian Structural Time series? | I would say Stan (disclosure: I am one of the Stan developers). For most of the models you mention, there is not going to be a Gibbs sampler with known full-conditional distributions for all of the pa | Coding resources: Accessible introductions to Bayesian Structural Time series?
I would say Stan (disclosure: I am one of the Stan developers). For most of the models you mention, there is not going to be a Gibbs sampler with known full-conditional distributions for all of the parameters and even for the exceptions, the... | Coding resources: Accessible introductions to Bayesian Structural Time series?
I would say Stan (disclosure: I am one of the Stan developers). For most of the models you mention, there is not going to be a Gibbs sampler with known full-conditional distributions for all of the pa |
37,495 | Multiple factor interactions with GAM in R? | I've had a similar problem, and I'm afraid I don't have a solution either, only a cheap workaround:
Since the by-variable in the smooth term doesn't seem to make the model assess the interaction (as in giving out a coefficient and S.E. for the interaction term, like a linear model would), what you get from something li... | Multiple factor interactions with GAM in R? | I've had a similar problem, and I'm afraid I don't have a solution either, only a cheap workaround:
Since the by-variable in the smooth term doesn't seem to make the model assess the interaction (as i | Multiple factor interactions with GAM in R?
I've had a similar problem, and I'm afraid I don't have a solution either, only a cheap workaround:
Since the by-variable in the smooth term doesn't seem to make the model assess the interaction (as in giving out a coefficient and S.E. for the interaction term, like a linear ... | Multiple factor interactions with GAM in R?
I've had a similar problem, and I'm afraid I don't have a solution either, only a cheap workaround:
Since the by-variable in the smooth term doesn't seem to make the model assess the interaction (as i |
37,496 | Illustration of functional gradient descent | Functional Gradient Descent
Functional Gradient Descent - Part 1 and Part 2 will give a brief introduction and theoretical illustration. Functional Gradient Descent was introduced in the NIPS publication Boosting Algorithms as Gradient Descent by Llew Mason, Jonathan Baxter, Peter Bartlett, and Marcus Frean in the year... | Illustration of functional gradient descent | Functional Gradient Descent
Functional Gradient Descent - Part 1 and Part 2 will give a brief introduction and theoretical illustration. Functional Gradient Descent was introduced in the NIPS publicat | Illustration of functional gradient descent
Functional Gradient Descent
Functional Gradient Descent - Part 1 and Part 2 will give a brief introduction and theoretical illustration. Functional Gradient Descent was introduced in the NIPS publication Boosting Algorithms as Gradient Descent by Llew Mason, Jonathan Baxter, ... | Illustration of functional gradient descent
Functional Gradient Descent
Functional Gradient Descent - Part 1 and Part 2 will give a brief introduction and theoretical illustration. Functional Gradient Descent was introduced in the NIPS publicat |
37,497 | Is feature transformation (power, log, Box-Cox) necessary in deep learning? | The rule of thumb is: the more data you have available, the less you have to care about feature engineering (which is basically inputting some prior knowledge into the model, based on the domain expertise).
Theoritically (with huge enough number of the samples) you could solve imagenet without using any convolutions, o... | Is feature transformation (power, log, Box-Cox) necessary in deep learning? | The rule of thumb is: the more data you have available, the less you have to care about feature engineering (which is basically inputting some prior knowledge into the model, based on the domain exper | Is feature transformation (power, log, Box-Cox) necessary in deep learning?
The rule of thumb is: the more data you have available, the less you have to care about feature engineering (which is basically inputting some prior knowledge into the model, based on the domain expertise).
Theoritically (with huge enough numbe... | Is feature transformation (power, log, Box-Cox) necessary in deep learning?
The rule of thumb is: the more data you have available, the less you have to care about feature engineering (which is basically inputting some prior knowledge into the model, based on the domain exper |
37,498 | Is feature transformation (power, log, Box-Cox) necessary in deep learning? | So the way I view feature engineering ala-box cox is that we have a model that requires normality, we don't have normal data, so we do a transform to get us to normal Data. So on the one hand its true that neural network do not require normalized data so why feature engineer. On the other hand, while a neural net might... | Is feature transformation (power, log, Box-Cox) necessary in deep learning? | So the way I view feature engineering ala-box cox is that we have a model that requires normality, we don't have normal data, so we do a transform to get us to normal Data. So on the one hand its true | Is feature transformation (power, log, Box-Cox) necessary in deep learning?
So the way I view feature engineering ala-box cox is that we have a model that requires normality, we don't have normal data, so we do a transform to get us to normal Data. So on the one hand its true that neural network do not require normaliz... | Is feature transformation (power, log, Box-Cox) necessary in deep learning?
So the way I view feature engineering ala-box cox is that we have a model that requires normality, we don't have normal data, so we do a transform to get us to normal Data. So on the one hand its true |
37,499 | Elastic net arbitrary alpha selection | The answer to a similar question here advises to follow the glmnet vignette (assuming you're using R):
foldid=sample(1:10,size=length(y),replace=TRUE)
cv1=cv.glmnet(x,y,foldid=foldid,alpha=1)
cv.5=cv.glmnet(x,y,foldid=foldid,alpha=.5)
cv0=cv.glmnet(x,y,foldid=foldid,alpha=0)
Keep the foldid fixed and assess a grid of ... | Elastic net arbitrary alpha selection | The answer to a similar question here advises to follow the glmnet vignette (assuming you're using R):
foldid=sample(1:10,size=length(y),replace=TRUE)
cv1=cv.glmnet(x,y,foldid=foldid,alpha=1)
cv.5=cv. | Elastic net arbitrary alpha selection
The answer to a similar question here advises to follow the glmnet vignette (assuming you're using R):
foldid=sample(1:10,size=length(y),replace=TRUE)
cv1=cv.glmnet(x,y,foldid=foldid,alpha=1)
cv.5=cv.glmnet(x,y,foldid=foldid,alpha=.5)
cv0=cv.glmnet(x,y,foldid=foldid,alpha=0)
Keep ... | Elastic net arbitrary alpha selection
The answer to a similar question here advises to follow the glmnet vignette (assuming you're using R):
foldid=sample(1:10,size=length(y),replace=TRUE)
cv1=cv.glmnet(x,y,foldid=foldid,alpha=1)
cv.5=cv. |
37,500 | GAM (mgcv): AIC vs Deviance Explained | The respective formulas for these two quantities are:
$$\text{deviance} = 2\log\mathcal{L}(\text{saturated model}\, |\, \text{data}) - 2\log\mathcal{L}(\text{model}\, |\, \text{data})$$
$$\text{AIC} = 2k- 2\log\mathcal{L}(\text{model}\, |\, \text{data})$$
where $\mathcal{L}$ is the likelihood and $k$ is the number of m... | GAM (mgcv): AIC vs Deviance Explained | The respective formulas for these two quantities are:
$$\text{deviance} = 2\log\mathcal{L}(\text{saturated model}\, |\, \text{data}) - 2\log\mathcal{L}(\text{model}\, |\, \text{data})$$
$$\text{AIC} = | GAM (mgcv): AIC vs Deviance Explained
The respective formulas for these two quantities are:
$$\text{deviance} = 2\log\mathcal{L}(\text{saturated model}\, |\, \text{data}) - 2\log\mathcal{L}(\text{model}\, |\, \text{data})$$
$$\text{AIC} = 2k- 2\log\mathcal{L}(\text{model}\, |\, \text{data})$$
where $\mathcal{L}$ is the... | GAM (mgcv): AIC vs Deviance Explained
The respective formulas for these two quantities are:
$$\text{deviance} = 2\log\mathcal{L}(\text{saturated model}\, |\, \text{data}) - 2\log\mathcal{L}(\text{model}\, |\, \text{data})$$
$$\text{AIC} = |
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