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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37,701 | Cramer-Rao bound for $\chi^2$ distribution parameter estimates | For small $\lambda$, lower bound for $\theta$ is $1/4 \times\nu^2 \lambda^{-3} \le \theta \lambda^{-1} \sigma^{-2}$
simplifying, $\theta$ varies with $\sigma^2 / \lambda^2$, thus $\theta\lambda^{-1}$ varies with $\sigma^2/\lambda$.
The inverse of $\theta \lambda^{-1} - 1$ will thus still increase with a lower value f... | Cramer-Rao bound for $\chi^2$ distribution parameter estimates | For small $\lambda$, lower bound for $\theta$ is $1/4 \times\nu^2 \lambda^{-3} \le \theta \lambda^{-1} \sigma^{-2}$
simplifying, $\theta$ varies with $\sigma^2 / \lambda^2$, thus $\theta\lambda^{-1}$ | Cramer-Rao bound for $\chi^2$ distribution parameter estimates
For small $\lambda$, lower bound for $\theta$ is $1/4 \times\nu^2 \lambda^{-3} \le \theta \lambda^{-1} \sigma^{-2}$
simplifying, $\theta$ varies with $\sigma^2 / \lambda^2$, thus $\theta\lambda^{-1}$ varies with $\sigma^2/\lambda$.
The inverse of $\theta ... | Cramer-Rao bound for $\chi^2$ distribution parameter estimates
For small $\lambda$, lower bound for $\theta$ is $1/4 \times\nu^2 \lambda^{-3} \le \theta \lambda^{-1} \sigma^{-2}$
simplifying, $\theta$ varies with $\sigma^2 / \lambda^2$, thus $\theta\lambda^{-1}$ |
37,702 | If coefficient variance is incorrect (for a regression parameter), does that mean the model's log-likelihood is incorrect? | Assume that you have $m$ observations for which there is no overlap, and $m_o$ observations that each appear twice in the sample. Your total sample size is therefore $N = m+2m_o$.
If observation $y_k$ is overlapped, then in the sample there exists another observation $\tilde y_k = 1-y_k$, but for which all explanatory ... | If coefficient variance is incorrect (for a regression parameter), does that mean the model's log-li | Assume that you have $m$ observations for which there is no overlap, and $m_o$ observations that each appear twice in the sample. Your total sample size is therefore $N = m+2m_o$.
If observation $y_k$ | If coefficient variance is incorrect (for a regression parameter), does that mean the model's log-likelihood is incorrect?
Assume that you have $m$ observations for which there is no overlap, and $m_o$ observations that each appear twice in the sample. Your total sample size is therefore $N = m+2m_o$.
If observation $y... | If coefficient variance is incorrect (for a regression parameter), does that mean the model's log-li
Assume that you have $m$ observations for which there is no overlap, and $m_o$ observations that each appear twice in the sample. Your total sample size is therefore $N = m+2m_o$.
If observation $y_k$ |
37,703 | Information gain is KL divergence | I found myself stuck with the same question in the past. Here's a series of relationships that may help to understand differences between Mutual Information (MI), Information Gain(IG) and Kullback–Leibler divergence (DKL). I used a simplified version of Wikipedia notation.
\begin{equation}
\begin{aligned}
MI(X,A) =& \... | Information gain is KL divergence | I found myself stuck with the same question in the past. Here's a series of relationships that may help to understand differences between Mutual Information (MI), Information Gain(IG) and Kullback–Lei | Information gain is KL divergence
I found myself stuck with the same question in the past. Here's a series of relationships that may help to understand differences between Mutual Information (MI), Information Gain(IG) and Kullback–Leibler divergence (DKL). I used a simplified version of Wikipedia notation.
\begin{equat... | Information gain is KL divergence
I found myself stuck with the same question in the past. Here's a series of relationships that may help to understand differences between Mutual Information (MI), Information Gain(IG) and Kullback–Lei |
37,704 | Information gain is KL divergence | $Q$ is "wrong" distribution, while $P$ is the "right" distribution. The length of a code $i$ under the "wrong" coding is $-log(q_{i})$. So, the average message length under the "wrong" coding is:
$-\sum p_{i} log(q_{i}) = H(P,Q)$
Under the "right" coding, the average message length is $H(P)$. Imagine that we are using ... | Information gain is KL divergence | $Q$ is "wrong" distribution, while $P$ is the "right" distribution. The length of a code $i$ under the "wrong" coding is $-log(q_{i})$. So, the average message length under the "wrong" coding is:
$-\s | Information gain is KL divergence
$Q$ is "wrong" distribution, while $P$ is the "right" distribution. The length of a code $i$ under the "wrong" coding is $-log(q_{i})$. So, the average message length under the "wrong" coding is:
$-\sum p_{i} log(q_{i}) = H(P,Q)$
Under the "right" coding, the average message length is ... | Information gain is KL divergence
$Q$ is "wrong" distribution, while $P$ is the "right" distribution. The length of a code $i$ under the "wrong" coding is $-log(q_{i})$. So, the average message length under the "wrong" coding is:
$-\s |
37,705 | Can I subsample a large dataset at every MCMC iteration? | About the subsampling strategies: just for example consider to have two observations $X_1 \sim N(\mu_1, \sigma_1^2)$ and $X_2 \sim N(\mu_2,\sigma_2^2)$ and consider to put some priors on the mean and variance. Let $\theta = (\mu_1, \mu_2, \sigma_1^2, \sigma_2^2)$, the posterior we want to evaluate is
$$
f(\theta|X_1,... | Can I subsample a large dataset at every MCMC iteration? | About the subsampling strategies: just for example consider to have two observations $X_1 \sim N(\mu_1, \sigma_1^2)$ and $X_2 \sim N(\mu_2,\sigma_2^2)$ and consider to put some priors on the mean and | Can I subsample a large dataset at every MCMC iteration?
About the subsampling strategies: just for example consider to have two observations $X_1 \sim N(\mu_1, \sigma_1^2)$ and $X_2 \sim N(\mu_2,\sigma_2^2)$ and consider to put some priors on the mean and variance. Let $\theta = (\mu_1, \mu_2, \sigma_1^2, \sigma_2^2)... | Can I subsample a large dataset at every MCMC iteration?
About the subsampling strategies: just for example consider to have two observations $X_1 \sim N(\mu_1, \sigma_1^2)$ and $X_2 \sim N(\mu_2,\sigma_2^2)$ and consider to put some priors on the mean and |
37,706 | Nonparametric mixture model and clusters | Answering to your point "Would you advise another point of view for this problem?", I would suggest that you actually have a look at your data. This can help you better plan what next steps to take. After all, the human eye-brain system is quite good in pattern recognition and you might be able to better decide upon th... | Nonparametric mixture model and clusters | Answering to your point "Would you advise another point of view for this problem?", I would suggest that you actually have a look at your data. This can help you better plan what next steps to take. A | Nonparametric mixture model and clusters
Answering to your point "Would you advise another point of view for this problem?", I would suggest that you actually have a look at your data. This can help you better plan what next steps to take. After all, the human eye-brain system is quite good in pattern recognition and y... | Nonparametric mixture model and clusters
Answering to your point "Would you advise another point of view for this problem?", I would suggest that you actually have a look at your data. This can help you better plan what next steps to take. A |
37,707 | Violation Proportionality Cox model - Repeat analysis? | Though I'm not an expert in survival analysis, I put here my suggestions and hope they will be helpful.
First of all, selection of variables looking at their p-values is a wrong way, especially when the model is aimed to make statistical inferences. You can read about that in multiple sources searching for "stepwise re... | Violation Proportionality Cox model - Repeat analysis? | Though I'm not an expert in survival analysis, I put here my suggestions and hope they will be helpful.
First of all, selection of variables looking at their p-values is a wrong way, especially when t | Violation Proportionality Cox model - Repeat analysis?
Though I'm not an expert in survival analysis, I put here my suggestions and hope they will be helpful.
First of all, selection of variables looking at their p-values is a wrong way, especially when the model is aimed to make statistical inferences. You can read ab... | Violation Proportionality Cox model - Repeat analysis?
Though I'm not an expert in survival analysis, I put here my suggestions and hope they will be helpful.
First of all, selection of variables looking at their p-values is a wrong way, especially when t |
37,708 | Gaussian mixture regression in higher dimensions | Your idea is exactly what I would recommend. To enforce the positive definiteness of the covariance matrices, just use the Cholesky decomposition $\Sigma_i = L_iL_i^T$, and minimise the objective with respect to $L_i$ instead of $\Sigma_i$. I refer to this article for more details.
Also, you may try to optimise the log... | Gaussian mixture regression in higher dimensions | Your idea is exactly what I would recommend. To enforce the positive definiteness of the covariance matrices, just use the Cholesky decomposition $\Sigma_i = L_iL_i^T$, and minimise the objective with | Gaussian mixture regression in higher dimensions
Your idea is exactly what I would recommend. To enforce the positive definiteness of the covariance matrices, just use the Cholesky decomposition $\Sigma_i = L_iL_i^T$, and minimise the objective with respect to $L_i$ instead of $\Sigma_i$. I refer to this article for mo... | Gaussian mixture regression in higher dimensions
Your idea is exactly what I would recommend. To enforce the positive definiteness of the covariance matrices, just use the Cholesky decomposition $\Sigma_i = L_iL_i^T$, and minimise the objective with |
37,709 | Interval censoring | The most important issue here is the understanding of censoring and which type applies in your situation. So for your problems 1. and 3., understand the context of your problem. This will help you define the appropriate censoring method.
The R output says that the first group of failures is in the interval (14,16]. Thi... | Interval censoring | The most important issue here is the understanding of censoring and which type applies in your situation. So for your problems 1. and 3., understand the context of your problem. This will help you def | Interval censoring
The most important issue here is the understanding of censoring and which type applies in your situation. So for your problems 1. and 3., understand the context of your problem. This will help you define the appropriate censoring method.
The R output says that the first group of failures is in the in... | Interval censoring
The most important issue here is the understanding of censoring and which type applies in your situation. So for your problems 1. and 3., understand the context of your problem. This will help you def |
37,710 | Overlapping sample t-test | You can try to use regression. Let $Y$, the response, be the performance measurtement. Then you make dummys for the persons and for the weeks. Now, this might get you more $x$-variables than observations, but maybe it can be salvaged, you can try to treat the person variable as a random effect, and week as a fixed effe... | Overlapping sample t-test | You can try to use regression. Let $Y$, the response, be the performance measurtement. Then you make dummys for the persons and for the weeks. Now, this might get you more $x$-variables than observati | Overlapping sample t-test
You can try to use regression. Let $Y$, the response, be the performance measurtement. Then you make dummys for the persons and for the weeks. Now, this might get you more $x$-variables than observations, but maybe it can be salvaged, you can try to treat the person variable as a random effect... | Overlapping sample t-test
You can try to use regression. Let $Y$, the response, be the performance measurtement. Then you make dummys for the persons and for the weeks. Now, this might get you more $x$-variables than observati |
37,711 | Overlapping sample t-test | You measure the performance for each person multiple times on different days and compare the two groups with the same people in each group. Therefore you should treat the two groups as non-independent, because it is very likely that the performance of one person will correlate on different days.
There are several optio... | Overlapping sample t-test | You measure the performance for each person multiple times on different days and compare the two groups with the same people in each group. Therefore you should treat the two groups as non-independent | Overlapping sample t-test
You measure the performance for each person multiple times on different days and compare the two groups with the same people in each group. Therefore you should treat the two groups as non-independent, because it is very likely that the performance of one person will correlate on different day... | Overlapping sample t-test
You measure the performance for each person multiple times on different days and compare the two groups with the same people in each group. Therefore you should treat the two groups as non-independent |
37,712 | Comparable training and test cross-entropies result in very different accuracies | It is suggested by Prof. Frank Harrell in my post that accuracy in percentage is an improper scoring rule in which the accuracy score is possibly optimized by the wrong model, and addition of a highly important predictor may cause the model less accurate. Besides, the accuracy scoring rule is high variance. On the ot... | Comparable training and test cross-entropies result in very different accuracies | It is suggested by Prof. Frank Harrell in my post that accuracy in percentage is an improper scoring rule in which the accuracy score is possibly optimized by the wrong model, and addition of a highl | Comparable training and test cross-entropies result in very different accuracies
It is suggested by Prof. Frank Harrell in my post that accuracy in percentage is an improper scoring rule in which the accuracy score is possibly optimized by the wrong model, and addition of a highly important predictor may cause the mod... | Comparable training and test cross-entropies result in very different accuracies
It is suggested by Prof. Frank Harrell in my post that accuracy in percentage is an improper scoring rule in which the accuracy score is possibly optimized by the wrong model, and addition of a highl |
37,713 | Determining the best correlated time series | how about you try a Two-Way Anova AND a pairwise test whether with your yearly data and/or the 5 year-period-intervals. You may also do this with the raw, normalized data or Box-Cox data.
Idea is, that you can look for any non-significant (for the reference station) difference between the distributions of precipitation... | Determining the best correlated time series | how about you try a Two-Way Anova AND a pairwise test whether with your yearly data and/or the 5 year-period-intervals. You may also do this with the raw, normalized data or Box-Cox data.
Idea is, tha | Determining the best correlated time series
how about you try a Two-Way Anova AND a pairwise test whether with your yearly data and/or the 5 year-period-intervals. You may also do this with the raw, normalized data or Box-Cox data.
Idea is, that you can look for any non-significant (for the reference station) differenc... | Determining the best correlated time series
how about you try a Two-Way Anova AND a pairwise test whether with your yearly data and/or the 5 year-period-intervals. You may also do this with the raw, normalized data or Box-Cox data.
Idea is, tha |
37,714 | Probability puzzle about zombies [closed] | I've actually written two papers on zombie epidemiology, so this is a question near and dear to my heart.
One suggestion I have is to use a dynamic model to estimate this, rather than just a time series (this is the technique used by the Smith? (the question mark is part of his name) paper).
There's a good site for ti... | Probability puzzle about zombies [closed] | I've actually written two papers on zombie epidemiology, so this is a question near and dear to my heart.
One suggestion I have is to use a dynamic model to estimate this, rather than just a time seri | Probability puzzle about zombies [closed]
I've actually written two papers on zombie epidemiology, so this is a question near and dear to my heart.
One suggestion I have is to use a dynamic model to estimate this, rather than just a time series (this is the technique used by the Smith? (the question mark is part of his... | Probability puzzle about zombies [closed]
I've actually written two papers on zombie epidemiology, so this is a question near and dear to my heart.
One suggestion I have is to use a dynamic model to estimate this, rather than just a time seri |
37,715 | Machine learning with ordered labels | This question seems to repeat itself every now and then (see here, for example), so I'll just summarize the answers and sources that have been accumulated in the time since this question was asked.
Redefine Objective
Ordinal Categorical Classification
A modified version of the cross-entropy, adjusted to ordinal target... | Machine learning with ordered labels | This question seems to repeat itself every now and then (see here, for example), so I'll just summarize the answers and sources that have been accumulated in the time since this question was asked.
Re | Machine learning with ordered labels
This question seems to repeat itself every now and then (see here, for example), so I'll just summarize the answers and sources that have been accumulated in the time since this question was asked.
Redefine Objective
Ordinal Categorical Classification
A modified version of the cros... | Machine learning with ordered labels
This question seems to repeat itself every now and then (see here, for example), so I'll just summarize the answers and sources that have been accumulated in the time since this question was asked.
Re |
37,716 | Yet another "Bayesian vs Maximum Likelihood" question | You should give a reference for your claim that the approximation obtained by simply replacing $\theta$ by its maximum likelihood estimator $\hat{\theta}$ is good. That approximation will forget about the uncertainty in the estimation of $\theta$, and might be a good approximation is some cases and bad in others. Tha... | Yet another "Bayesian vs Maximum Likelihood" question | You should give a reference for your claim that the approximation obtained by simply replacing $\theta$ by its maximum likelihood estimator $\hat{\theta}$ is good. That approximation will forget abou | Yet another "Bayesian vs Maximum Likelihood" question
You should give a reference for your claim that the approximation obtained by simply replacing $\theta$ by its maximum likelihood estimator $\hat{\theta}$ is good. That approximation will forget about the uncertainty in the estimation of $\theta$, and might be a go... | Yet another "Bayesian vs Maximum Likelihood" question
You should give a reference for your claim that the approximation obtained by simply replacing $\theta$ by its maximum likelihood estimator $\hat{\theta}$ is good. That approximation will forget abou |
37,717 | Yet another "Bayesian vs Maximum Likelihood" question | I would like to wax philosophically on @kjetil's answer, and specifically this statement:
That approximation will forget about the uncertainty in the estimation of θ, and might be a good approximation is some cases and bad in others. That must be evaluated on a case-by-case basis.
The reason that we can use the MLE t... | Yet another "Bayesian vs Maximum Likelihood" question | I would like to wax philosophically on @kjetil's answer, and specifically this statement:
That approximation will forget about the uncertainty in the estimation of θ, and might be a good approximatio | Yet another "Bayesian vs Maximum Likelihood" question
I would like to wax philosophically on @kjetil's answer, and specifically this statement:
That approximation will forget about the uncertainty in the estimation of θ, and might be a good approximation is some cases and bad in others. That must be evaluated on a cas... | Yet another "Bayesian vs Maximum Likelihood" question
I would like to wax philosophically on @kjetil's answer, and specifically this statement:
That approximation will forget about the uncertainty in the estimation of θ, and might be a good approximatio |
37,718 | Selecting an appropriate machine learning algorithm? | are there any rules of the format "IF feature X has property Z THEN do Y"?
Yes, there are such rules. Or rather, if x then is is sensible to try y and z and avoid w.
However, what is sensible and what is not depends on
your application (influences e.g. expected complexity of the problem)
the size of the data set: how... | Selecting an appropriate machine learning algorithm? | are there any rules of the format "IF feature X has property Z THEN do Y"?
Yes, there are such rules. Or rather, if x then is is sensible to try y and z and avoid w.
However, what is sensible and wha | Selecting an appropriate machine learning algorithm?
are there any rules of the format "IF feature X has property Z THEN do Y"?
Yes, there are such rules. Or rather, if x then is is sensible to try y and z and avoid w.
However, what is sensible and what is not depends on
your application (influences e.g. expected com... | Selecting an appropriate machine learning algorithm?
are there any rules of the format "IF feature X has property Z THEN do Y"?
Yes, there are such rules. Or rather, if x then is is sensible to try y and z and avoid w.
However, what is sensible and wha |
37,719 | Selecting an appropriate machine learning algorithm? | There is a classic paper (Wolpert, 1996) that discusses no-free-lunch theorem mentioned above. The paper can be found here. But according to the paper and most practitioners, "there are [rarely]
a priori distinctions between learning algorithms." Note: I replaced "no" with "rarely".
Reference
Wolpert, D. H. (1996). Th... | Selecting an appropriate machine learning algorithm? | There is a classic paper (Wolpert, 1996) that discusses no-free-lunch theorem mentioned above. The paper can be found here. But according to the paper and most practitioners, "there are [rarely]
a pr | Selecting an appropriate machine learning algorithm?
There is a classic paper (Wolpert, 1996) that discusses no-free-lunch theorem mentioned above. The paper can be found here. But according to the paper and most practitioners, "there are [rarely]
a priori distinctions between learning algorithms." Note: I replaced "n... | Selecting an appropriate machine learning algorithm?
There is a classic paper (Wolpert, 1996) that discusses no-free-lunch theorem mentioned above. The paper can be found here. But according to the paper and most practitioners, "there are [rarely]
a pr |
37,720 | Non-parametric correlation for continuous and dichotomous variables [closed] | Consider Rank Biserial Correlation.
"A formula is developed for the correlation between a ranking (possibly including ties) and a dichotomy, with limits which are always ±1. This formula is shown to be equivalent both to Kendall'sτ and Spearman's ρ"
Reference: E. E. Cureton (1956) "Rank Biserial Correlation", Psychomet... | Non-parametric correlation for continuous and dichotomous variables [closed] | Consider Rank Biserial Correlation.
"A formula is developed for the correlation between a ranking (possibly including ties) and a dichotomy, with limits which are always ±1. This formula is shown to b | Non-parametric correlation for continuous and dichotomous variables [closed]
Consider Rank Biserial Correlation.
"A formula is developed for the correlation between a ranking (possibly including ties) and a dichotomy, with limits which are always ±1. This formula is shown to be equivalent both to Kendall'sτ and Spearma... | Non-parametric correlation for continuous and dichotomous variables [closed]
Consider Rank Biserial Correlation.
"A formula is developed for the correlation between a ranking (possibly including ties) and a dichotomy, with limits which are always ±1. This formula is shown to b |
37,721 | Failing at linear regression / prediction on a real data set | This is probably calls for simultaneous equation modeling, rather than linear regression.
The probability of success depends on two separate equations, one measuring the quality of the opponent, person and machine, the other measuring the quality of the self, person and machine. They directly oppose each other, but on... | Failing at linear regression / prediction on a real data set | This is probably calls for simultaneous equation modeling, rather than linear regression.
The probability of success depends on two separate equations, one measuring the quality of the opponent, perso | Failing at linear regression / prediction on a real data set
This is probably calls for simultaneous equation modeling, rather than linear regression.
The probability of success depends on two separate equations, one measuring the quality of the opponent, person and machine, the other measuring the quality of the self,... | Failing at linear regression / prediction on a real data set
This is probably calls for simultaneous equation modeling, rather than linear regression.
The probability of success depends on two separate equations, one measuring the quality of the opponent, perso |
37,722 | Validation of a questionnaire in a new population | @suzi One of the properties upon which Rasch analysis is based is that measures are invariant to subgroups. This property supports the development of computer adaptive testing and test equating. If the this invariance of measure holds true in a population, then there is no differential item functioning (DIF). To ass... | Validation of a questionnaire in a new population | @suzi One of the properties upon which Rasch analysis is based is that measures are invariant to subgroups. This property supports the development of computer adaptive testing and test equating. If | Validation of a questionnaire in a new population
@suzi One of the properties upon which Rasch analysis is based is that measures are invariant to subgroups. This property supports the development of computer adaptive testing and test equating. If the this invariance of measure holds true in a population, then there... | Validation of a questionnaire in a new population
@suzi One of the properties upon which Rasch analysis is based is that measures are invariant to subgroups. This property supports the development of computer adaptive testing and test equating. If |
37,723 | Probabilities from Logistic Regression | If I understand your question correctly, you have predicted the probability for each individual, but want to know the average probability of a segment of those individuals? For instance you have 1000 individuals with the average rate of 65% but only 300 have blue eyes, what is the average rate of those with blue eyes? ... | Probabilities from Logistic Regression | If I understand your question correctly, you have predicted the probability for each individual, but want to know the average probability of a segment of those individuals? For instance you have 1000 | Probabilities from Logistic Regression
If I understand your question correctly, you have predicted the probability for each individual, but want to know the average probability of a segment of those individuals? For instance you have 1000 individuals with the average rate of 65% but only 300 have blue eyes, what is the... | Probabilities from Logistic Regression
If I understand your question correctly, you have predicted the probability for each individual, but want to know the average probability of a segment of those individuals? For instance you have 1000 |
37,724 | When does it make sense to reject/accept an hypothesis? | I really like your rainbow versions of my clouds, and may 'borrow' them for a future version of my paper. Thank you!
Your questions are not entirely clear to me, so I will paraphrase them. If they are not what you had in mind them my answers will be misdirected!
Are there situations where rejection of the hypothesis l... | When does it make sense to reject/accept an hypothesis? | I really like your rainbow versions of my clouds, and may 'borrow' them for a future version of my paper. Thank you!
Your questions are not entirely clear to me, so I will paraphrase them. If they are | When does it make sense to reject/accept an hypothesis?
I really like your rainbow versions of my clouds, and may 'borrow' them for a future version of my paper. Thank you!
Your questions are not entirely clear to me, so I will paraphrase them. If they are not what you had in mind them my answers will be misdirected!
... | When does it make sense to reject/accept an hypothesis?
I really like your rainbow versions of my clouds, and may 'borrow' them for a future version of my paper. Thank you!
Your questions are not entirely clear to me, so I will paraphrase them. If they are |
37,725 | Generating causally dependent random variables | It seems that in order to reproduce the joint distribution $\rho(a,v)$, you should select new $a$ not only based on $v$, but based on the old $a$ also:
$a_{i+1} \sim \rho'(a_{i+1}|a_i, v_i)$
The question (to which I don't know the answer yet) is how to find $\rho'$ which produces $\rho$.
UPD:
You are to solve the follo... | Generating causally dependent random variables | It seems that in order to reproduce the joint distribution $\rho(a,v)$, you should select new $a$ not only based on $v$, but based on the old $a$ also:
$a_{i+1} \sim \rho'(a_{i+1}|a_i, v_i)$
The quest | Generating causally dependent random variables
It seems that in order to reproduce the joint distribution $\rho(a,v)$, you should select new $a$ not only based on $v$, but based on the old $a$ also:
$a_{i+1} \sim \rho'(a_{i+1}|a_i, v_i)$
The question (to which I don't know the answer yet) is how to find $\rho'$ which p... | Generating causally dependent random variables
It seems that in order to reproduce the joint distribution $\rho(a,v)$, you should select new $a$ not only based on $v$, but based on the old $a$ also:
$a_{i+1} \sim \rho'(a_{i+1}|a_i, v_i)$
The quest |
37,726 | Generating causally dependent random variables | Doesn't the gps data contain position $p$? I would have thought that, not only is $v_{i+1}$ dependent upon $v_{i}$ and $a_{i}$ but $a_{i+1}$ would also be dependent upon $p_{i}$. Consider: in any road network there are bottlenecks, speed limits, signals, intersections, steep gradients, etc. that are geolocated. So ... | Generating causally dependent random variables | Doesn't the gps data contain position $p$? I would have thought that, not only is $v_{i+1}$ dependent upon $v_{i}$ and $a_{i}$ but $a_{i+1}$ would also be dependent upon $p_{i}$. Consider: in any r | Generating causally dependent random variables
Doesn't the gps data contain position $p$? I would have thought that, not only is $v_{i+1}$ dependent upon $v_{i}$ and $a_{i}$ but $a_{i+1}$ would also be dependent upon $p_{i}$. Consider: in any road network there are bottlenecks, speed limits, signals, intersections, ... | Generating causally dependent random variables
Doesn't the gps data contain position $p$? I would have thought that, not only is $v_{i+1}$ dependent upon $v_{i}$ and $a_{i}$ but $a_{i+1}$ would also be dependent upon $p_{i}$. Consider: in any r |
37,727 | Can I use a confidence interval of poisson mean for its variance | Your approach is basically correct but heavily depends on the strong distributional assumption you are making. If it is violated, even for very large samples, the confidence regions wont have the stated coverage probabilities. That's why statisticians try to avoid such reasoning if there are more robust methods availab... | Can I use a confidence interval of poisson mean for its variance | Your approach is basically correct but heavily depends on the strong distributional assumption you are making. If it is violated, even for very large samples, the confidence regions wont have the stat | Can I use a confidence interval of poisson mean for its variance
Your approach is basically correct but heavily depends on the strong distributional assumption you are making. If it is violated, even for very large samples, the confidence regions wont have the stated coverage probabilities. That's why statisticians try... | Can I use a confidence interval of poisson mean for its variance
Your approach is basically correct but heavily depends on the strong distributional assumption you are making. If it is violated, even for very large samples, the confidence regions wont have the stat |
37,728 | Regression of data that includes a date | It sounds to like you need use time series methods, such as ARMA or ARIMA, that let you calculate a regression using time as an independent variable without violating the independent observations assumption of OLS.
You may want to try a two step analysis:
- first use time as a single predictor variable and use a suit... | Regression of data that includes a date | It sounds to like you need use time series methods, such as ARMA or ARIMA, that let you calculate a regression using time as an independent variable without violating the independent observations assu | Regression of data that includes a date
It sounds to like you need use time series methods, such as ARMA or ARIMA, that let you calculate a regression using time as an independent variable without violating the independent observations assumption of OLS.
You may want to try a two step analysis:
- first use time as a ... | Regression of data that includes a date
It sounds to like you need use time series methods, such as ARMA or ARIMA, that let you calculate a regression using time as an independent variable without violating the independent observations assu |
37,729 | Regression of data that includes a date | There's several ways. An option is to convert dates into days after the very first day. Also, you could have have additional variables of days of the week (trends across the week) and the month (to see trends in certain times of the year). By doing so, you can use multiple regression.
To get the variable "# of days af... | Regression of data that includes a date | There's several ways. An option is to convert dates into days after the very first day. Also, you could have have additional variables of days of the week (trends across the week) and the month (to se | Regression of data that includes a date
There's several ways. An option is to convert dates into days after the very first day. Also, you could have have additional variables of days of the week (trends across the week) and the month (to see trends in certain times of the year). By doing so, you can use multiple regres... | Regression of data that includes a date
There's several ways. An option is to convert dates into days after the very first day. Also, you could have have additional variables of days of the week (trends across the week) and the month (to se |
37,730 | Regression of data that includes a date | I can advise you to use non-linear function for time variable because the prices fall is lesser with each additional time unit. Otherwise the price would finally fall below zero. Moreover, there may be periods when the trend changed up. Thus I recommend to use cubic splines for time variable.
Experience whispers me tha... | Regression of data that includes a date | I can advise you to use non-linear function for time variable because the prices fall is lesser with each additional time unit. Otherwise the price would finally fall below zero. Moreover, there may b | Regression of data that includes a date
I can advise you to use non-linear function for time variable because the prices fall is lesser with each additional time unit. Otherwise the price would finally fall below zero. Moreover, there may be periods when the trend changed up. Thus I recommend to use cubic splines for t... | Regression of data that includes a date
I can advise you to use non-linear function for time variable because the prices fall is lesser with each additional time unit. Otherwise the price would finally fall below zero. Moreover, there may b |
37,731 | Regression of data that includes a date | Pick a reference date, say 1/1/2010, and make a new variable time that is the difference between the date and the reference date, where the difference is computed in, say, days.
Now run a linear regression (or something similar) with time and supplier as the two predictor variables and price as the response variable.... | Regression of data that includes a date | Pick a reference date, say 1/1/2010, and make a new variable time that is the difference between the date and the reference date, where the difference is computed in, say, days.
Now run a linear reg | Regression of data that includes a date
Pick a reference date, say 1/1/2010, and make a new variable time that is the difference between the date and the reference date, where the difference is computed in, say, days.
Now run a linear regression (or something similar) with time and supplier as the two predictor varia... | Regression of data that includes a date
Pick a reference date, say 1/1/2010, and make a new variable time that is the difference between the date and the reference date, where the difference is computed in, say, days.
Now run a linear reg |
37,732 | Are there any alternatives to simulation for determining the distribution of number of events from two dependent non-homogeneous Poisson processes? | That's an interesting problem. I'm not sure to have cought all you mean, but have you thought about reformulating some of your problems as hypothesis tests ? Like:
null hypothesis H0: $x > y$
alternative hypothesis H1: $x \le y$
and then to perform a likelihood ratio test ? Then the extracted p-value tells you if whe... | Are there any alternatives to simulation for determining the distribution of number of events from t | That's an interesting problem. I'm not sure to have cought all you mean, but have you thought about reformulating some of your problems as hypothesis tests ? Like:
null hypothesis H0: $x > y$
alterna | Are there any alternatives to simulation for determining the distribution of number of events from two dependent non-homogeneous Poisson processes?
That's an interesting problem. I'm not sure to have cought all you mean, but have you thought about reformulating some of your problems as hypothesis tests ? Like:
null hy... | Are there any alternatives to simulation for determining the distribution of number of events from t
That's an interesting problem. I'm not sure to have cought all you mean, but have you thought about reformulating some of your problems as hypothesis tests ? Like:
null hypothesis H0: $x > y$
alterna |
37,733 | Are there any alternatives to simulation for determining the distribution of number of events from two dependent non-homogeneous Poisson processes? | I first address 2 problems with the question:
The so-called time inhomogeneous factors preclude the process from being Poisson, because the number of goals in some time interval is not independent of the earlier number of goals. In other words, the transition rate is state dependent. Even the linked article (P.7) call... | Are there any alternatives to simulation for determining the distribution of number of events from t | I first address 2 problems with the question:
The so-called time inhomogeneous factors preclude the process from being Poisson, because the number of goals in some time interval is not independent of | Are there any alternatives to simulation for determining the distribution of number of events from two dependent non-homogeneous Poisson processes?
I first address 2 problems with the question:
The so-called time inhomogeneous factors preclude the process from being Poisson, because the number of goals in some time in... | Are there any alternatives to simulation for determining the distribution of number of events from t
I first address 2 problems with the question:
The so-called time inhomogeneous factors preclude the process from being Poisson, because the number of goals in some time interval is not independent of |
37,734 | Finding correlations in longitudinal data analysis | There are two main options available (actually more but let us for simplicity only mention two):
First, you can do standard regression techniques (e.g. linear regression or methods for discrete choice), controlling for example for the length and content of the videos watched (concern two). Of course there will be intr... | Finding correlations in longitudinal data analysis | There are two main options available (actually more but let us for simplicity only mention two):
First, you can do standard regression techniques (e.g. linear regression or methods for discrete choic | Finding correlations in longitudinal data analysis
There are two main options available (actually more but let us for simplicity only mention two):
First, you can do standard regression techniques (e.g. linear regression or methods for discrete choice), controlling for example for the length and content of the videos ... | Finding correlations in longitudinal data analysis
There are two main options available (actually more but let us for simplicity only mention two):
First, you can do standard regression techniques (e.g. linear regression or methods for discrete choic |
37,735 | glmnet, categorical variable, group lasso? | As far as I am aware glmnet doesn't have this feature implemented yet. @Glen_b's suggestion of using type.multinomial is used to group variables across all responses in a multinomial model, but there's no way of grouping independent variables in a model. see
https://cran.r-project.org/web/packages/grplasso/grplasso.pd... | glmnet, categorical variable, group lasso? | As far as I am aware glmnet doesn't have this feature implemented yet. @Glen_b's suggestion of using type.multinomial is used to group variables across all responses in a multinomial model, but there' | glmnet, categorical variable, group lasso?
As far as I am aware glmnet doesn't have this feature implemented yet. @Glen_b's suggestion of using type.multinomial is used to group variables across all responses in a multinomial model, but there's no way of grouping independent variables in a model. see
https://cran.r-pr... | glmnet, categorical variable, group lasso?
As far as I am aware glmnet doesn't have this feature implemented yet. @Glen_b's suggestion of using type.multinomial is used to group variables across all responses in a multinomial model, but there' |
37,736 | Test for variation using random effects model | There are several things you could do to test whether there is genetic variance.
First, however, I wonder why you want separate models for "each combination of Treat1 and Treat2 (AP,AQ,BP,BQ)"? I don't know anything about the substantive area of application here, and I may be misunderstanding your data, but I think you... | Test for variation using random effects model | There are several things you could do to test whether there is genetic variance.
First, however, I wonder why you want separate models for "each combination of Treat1 and Treat2 (AP,AQ,BP,BQ)"? I don' | Test for variation using random effects model
There are several things you could do to test whether there is genetic variance.
First, however, I wonder why you want separate models for "each combination of Treat1 and Treat2 (AP,AQ,BP,BQ)"? I don't know anything about the substantive area of application here, and I may ... | Test for variation using random effects model
There are several things you could do to test whether there is genetic variance.
First, however, I wonder why you want separate models for "each combination of Treat1 and Treat2 (AP,AQ,BP,BQ)"? I don' |
37,737 | Analyze a football match: similar players with DBSCAN and similar trajectories with TRACLUS | There are 2 questions there (1st point is not a question). All answers are below.
Q1: How can you cluster players that pass the ball to each other more often?
In my view this is a loaded task that is better broken into the following:
Identify whether a player is passing a ball. You have to look at the distribution of ... | Analyze a football match: similar players with DBSCAN and similar trajectories with TRACLUS | There are 2 questions there (1st point is not a question). All answers are below.
Q1: How can you cluster players that pass the ball to each other more often?
In my view this is a loaded task that is | Analyze a football match: similar players with DBSCAN and similar trajectories with TRACLUS
There are 2 questions there (1st point is not a question). All answers are below.
Q1: How can you cluster players that pass the ball to each other more often?
In my view this is a loaded task that is better broken into the follo... | Analyze a football match: similar players with DBSCAN and similar trajectories with TRACLUS
There are 2 questions there (1st point is not a question). All answers are below.
Q1: How can you cluster players that pass the ball to each other more often?
In my view this is a loaded task that is |
37,738 | "Monte Carlo Kalman Filter" vs Unscented Kalman Filter | I would recommend looking at this paper:
Hommels A, Murakami A, Nishimura SI. A comparison of the ensemble Kalman filter with the unscented Kalman filter: application to the construction of a road embankment. Geotechniek. 2009;13(1):52.
I believe the authors of the above paper conclude that MCKF or EnKF is better than... | "Monte Carlo Kalman Filter" vs Unscented Kalman Filter | I would recommend looking at this paper:
Hommels A, Murakami A, Nishimura SI. A comparison of the ensemble Kalman filter with the unscented Kalman filter: application to the construction of a road em | "Monte Carlo Kalman Filter" vs Unscented Kalman Filter
I would recommend looking at this paper:
Hommels A, Murakami A, Nishimura SI. A comparison of the ensemble Kalman filter with the unscented Kalman filter: application to the construction of a road embankment. Geotechniek. 2009;13(1):52.
I believe the authors of th... | "Monte Carlo Kalman Filter" vs Unscented Kalman Filter
I would recommend looking at this paper:
Hommels A, Murakami A, Nishimura SI. A comparison of the ensemble Kalman filter with the unscented Kalman filter: application to the construction of a road em |
37,739 | Sample size with respect to prediction in classification and regression | Basically, I think you ask intuitively how sample size affects machine learning techniques. So, the real factor that affects the required sample sizes is dimensionality of the space that data live in, and its sparseness. I will give you two examples, because I find it hard to summarise everything in one...
Let's say ... | Sample size with respect to prediction in classification and regression | Basically, I think you ask intuitively how sample size affects machine learning techniques. So, the real factor that affects the required sample sizes is dimensionality of the space that data live in, | Sample size with respect to prediction in classification and regression
Basically, I think you ask intuitively how sample size affects machine learning techniques. So, the real factor that affects the required sample sizes is dimensionality of the space that data live in, and its sparseness. I will give you two example... | Sample size with respect to prediction in classification and regression
Basically, I think you ask intuitively how sample size affects machine learning techniques. So, the real factor that affects the required sample sizes is dimensionality of the space that data live in, |
37,740 | Sample size with respect to prediction in classification and regression | I dont understand the question fully. Generally a bigger sample will yield (for example) a better classification. Unless bigger means bad quality observations. A small sample will make a lot of models useless. For example since tree based models are a sort of "divde and conquer" approach their efficiency depends a lot ... | Sample size with respect to prediction in classification and regression | I dont understand the question fully. Generally a bigger sample will yield (for example) a better classification. Unless bigger means bad quality observations. A small sample will make a lot of models | Sample size with respect to prediction in classification and regression
I dont understand the question fully. Generally a bigger sample will yield (for example) a better classification. Unless bigger means bad quality observations. A small sample will make a lot of models useless. For example since tree based models ar... | Sample size with respect to prediction in classification and regression
I dont understand the question fully. Generally a bigger sample will yield (for example) a better classification. Unless bigger means bad quality observations. A small sample will make a lot of models |
37,741 | How can one perform a two-group binomial power analysis without using normal approximations? | This is not an answer. It is a community wiki that people may edit as they look for the answer.
G*power 3 can perform (approximations) of these analyses (per this site). The canonical reference for that software provides a reference for performing (at least some) of these types of power analyses as Cohen, 1988 chapte... | How can one perform a two-group binomial power analysis without using normal approximations? | This is not an answer. It is a community wiki that people may edit as they look for the answer.
G*power 3 can perform (approximations) of these analyses (per this site). The canonical reference for | How can one perform a two-group binomial power analysis without using normal approximations?
This is not an answer. It is a community wiki that people may edit as they look for the answer.
G*power 3 can perform (approximations) of these analyses (per this site). The canonical reference for that software provides a re... | How can one perform a two-group binomial power analysis without using normal approximations?
This is not an answer. It is a community wiki that people may edit as they look for the answer.
G*power 3 can perform (approximations) of these analyses (per this site). The canonical reference for |
37,742 | Causal identification and penalized splines | "Clean identification" of regression parameters is not an established concept. I believe what the reviewer means by this is that you should specify a parameter which is interpretable, testable, of low dimensionality, and for which the analysis is decently powered to detect so that an unbiased estimate can be obtained w... | Causal identification and penalized splines | "Clean identification" of regression parameters is not an established concept. I believe what the reviewer means by this is that you should specify a parameter which is interpretable, testable, of low | Causal identification and penalized splines
"Clean identification" of regression parameters is not an established concept. I believe what the reviewer means by this is that you should specify a parameter which is interpretable, testable, of low dimensionality, and for which the analysis is decently powered to detect so... | Causal identification and penalized splines
"Clean identification" of regression parameters is not an established concept. I believe what the reviewer means by this is that you should specify a parameter which is interpretable, testable, of low |
37,743 | Credibility Intervals | The problem of comparing credible sets and confidence intervals is that they are not apples to apples or apples to oranges comparisons. They are an apples to tractors comparison. They are only substitutes for one another in certain circumstances.
The primary use of a confidence interval is in scientific research. Al... | Credibility Intervals | The problem of comparing credible sets and confidence intervals is that they are not apples to apples or apples to oranges comparisons. They are an apples to tractors comparison. They are only subst | Credibility Intervals
The problem of comparing credible sets and confidence intervals is that they are not apples to apples or apples to oranges comparisons. They are an apples to tractors comparison. They are only substitutes for one another in certain circumstances.
The primary use of a confidence interval is in sc... | Credibility Intervals
The problem of comparing credible sets and confidence intervals is that they are not apples to apples or apples to oranges comparisons. They are an apples to tractors comparison. They are only subst |
37,744 | Credibility Intervals | A classic example is when you have tested a drug versus a placebo in a randomized clinical trial of 1 year duration and there were 1000 patients in each group. An adverse event that people were concerned could be a side effect of the treatment occurred in 0 patients in the treatment group and 0 patients in the placebo ... | Credibility Intervals | A classic example is when you have tested a drug versus a placebo in a randomized clinical trial of 1 year duration and there were 1000 patients in each group. An adverse event that people were concer | Credibility Intervals
A classic example is when you have tested a drug versus a placebo in a randomized clinical trial of 1 year duration and there were 1000 patients in each group. An adverse event that people were concerned could be a side effect of the treatment occurred in 0 patients in the treatment group and 0 pa... | Credibility Intervals
A classic example is when you have tested a drug versus a placebo in a randomized clinical trial of 1 year duration and there were 1000 patients in each group. An adverse event that people were concer |
37,745 | Credibility Intervals | Bjorn's answer suggests a frequentist confidence procedure cannot handle sparse data, nor can it incorporate historical data. To illustrate this Bjorn provides the TGN1412 example,
(see e.g. pages 2 and 92 to 94 here
or Senn, S. (2008). Lessons from TGN1412 and TARGET: Implications for
observational studies and meta-... | Credibility Intervals | Bjorn's answer suggests a frequentist confidence procedure cannot handle sparse data, nor can it incorporate historical data. To illustrate this Bjorn provides the TGN1412 example,
(see e.g. pages 2 | Credibility Intervals
Bjorn's answer suggests a frequentist confidence procedure cannot handle sparse data, nor can it incorporate historical data. To illustrate this Bjorn provides the TGN1412 example,
(see e.g. pages 2 and 92 to 94 here
or Senn, S. (2008). Lessons from TGN1412 and TARGET: Implications for
observati... | Credibility Intervals
Bjorn's answer suggests a frequentist confidence procedure cannot handle sparse data, nor can it incorporate historical data. To illustrate this Bjorn provides the TGN1412 example,
(see e.g. pages 2 |
37,746 | Credibility Intervals | If you concern about the recovery rate of a certain disease, credible interval is what you need when you want to say
There is a 95% chance that the recovery rate is between X and Y.
You cannot say this using confidence interval. With a 95% confidence interval, you can only say
There is a 95% chance that the next set... | Credibility Intervals | If you concern about the recovery rate of a certain disease, credible interval is what you need when you want to say
There is a 95% chance that the recovery rate is between X and Y.
You cannot say t | Credibility Intervals
If you concern about the recovery rate of a certain disease, credible interval is what you need when you want to say
There is a 95% chance that the recovery rate is between X and Y.
You cannot say this using confidence interval. With a 95% confidence interval, you can only say
There is a 95% ch... | Credibility Intervals
If you concern about the recovery rate of a certain disease, credible interval is what you need when you want to say
There is a 95% chance that the recovery rate is between X and Y.
You cannot say t |
37,747 | Relative advantages of multiple imputation and expectation maximization (EM) | Whether or not it makes sense to use GLMs depends on the distribution of $y$. I'd be inclined to use a nonlinear least squares model for the whole thing.
So if your regression model is $a = Z\alpha+\nu$ where $Z$ are the predictors and $\alpha$ are the parameters in the regression model for $a$, and your model for $b$... | Relative advantages of multiple imputation and expectation maximization (EM) | Whether or not it makes sense to use GLMs depends on the distribution of $y$. I'd be inclined to use a nonlinear least squares model for the whole thing.
So if your regression model is $a = Z\alpha+\ | Relative advantages of multiple imputation and expectation maximization (EM)
Whether or not it makes sense to use GLMs depends on the distribution of $y$. I'd be inclined to use a nonlinear least squares model for the whole thing.
So if your regression model is $a = Z\alpha+\nu$ where $Z$ are the predictors and $\alph... | Relative advantages of multiple imputation and expectation maximization (EM)
Whether or not it makes sense to use GLMs depends on the distribution of $y$. I'd be inclined to use a nonlinear least squares model for the whole thing.
So if your regression model is $a = Z\alpha+\ |
37,748 | The gradient of a bivariate probit model | Let's take a step back and solve a simpler problem - how do derivatives with respect to a variable in the limits of the integral work, at least when everything is sufficiently nice? Let's take a very basic approach:
$\frac{d}{dz} \int_{a}^{h(z)} g(x) \,dx$
Let $\frac{dG}{dz}=g(z)$.
$=\frac{d}{dz} [G(h(z))-G(a)]=\frac{d... | The gradient of a bivariate probit model | Let's take a step back and solve a simpler problem - how do derivatives with respect to a variable in the limits of the integral work, at least when everything is sufficiently nice? Let's take a very | The gradient of a bivariate probit model
Let's take a step back and solve a simpler problem - how do derivatives with respect to a variable in the limits of the integral work, at least when everything is sufficiently nice? Let's take a very basic approach:
$\frac{d}{dz} \int_{a}^{h(z)} g(x) \,dx$
Let $\frac{dG}{dz}=g(z... | The gradient of a bivariate probit model
Let's take a step back and solve a simpler problem - how do derivatives with respect to a variable in the limits of the integral work, at least when everything is sufficiently nice? Let's take a very |
37,749 | I am getting a number below zero when calculating out two standard deviations from the mean. Is this ok? | It appears unlikely to me that the question would require you to calculate two standard deviations of the data out from the mean - especially given that your data are unlikely to be even symmetric, much less normally distributed (since they are discrete). I see no interesting question that could really be answered by t... | I am getting a number below zero when calculating out two standard deviations from the mean. Is this | It appears unlikely to me that the question would require you to calculate two standard deviations of the data out from the mean - especially given that your data are unlikely to be even symmetric, mu | I am getting a number below zero when calculating out two standard deviations from the mean. Is this ok?
It appears unlikely to me that the question would require you to calculate two standard deviations of the data out from the mean - especially given that your data are unlikely to be even symmetric, much less normall... | I am getting a number below zero when calculating out two standard deviations from the mean. Is this
It appears unlikely to me that the question would require you to calculate two standard deviations of the data out from the mean - especially given that your data are unlikely to be even symmetric, mu |
37,750 | Should the likelihood function be increasing in every step of the EM algorithm? | The estimator may increase or decrease during each iteration however the likelihood must increase.
You should make sure your likelihood is increasing at each step and see if you are converging to the same value. | Should the likelihood function be increasing in every step of the EM algorithm? | The estimator may increase or decrease during each iteration however the likelihood must increase.
You should make sure your likelihood is increasing at each step and see if you are converging to the | Should the likelihood function be increasing in every step of the EM algorithm?
The estimator may increase or decrease during each iteration however the likelihood must increase.
You should make sure your likelihood is increasing at each step and see if you are converging to the same value. | Should the likelihood function be increasing in every step of the EM algorithm?
The estimator may increase or decrease during each iteration however the likelihood must increase.
You should make sure your likelihood is increasing at each step and see if you are converging to the |
37,751 | Should the likelihood function be increasing in every step of the EM algorithm? | Perhaps not relevant in this case but note that if the E-step is estimated, with Monte Carlo methods or another approximation, it is possible for the likelihood to decrease.
A thought that might be relevant is that the EM does not converge towards the global maxima but to a local.
For more details see section 3 of
"On ... | Should the likelihood function be increasing in every step of the EM algorithm? | Perhaps not relevant in this case but note that if the E-step is estimated, with Monte Carlo methods or another approximation, it is possible for the likelihood to decrease.
A thought that might be re | Should the likelihood function be increasing in every step of the EM algorithm?
Perhaps not relevant in this case but note that if the E-step is estimated, with Monte Carlo methods or another approximation, it is possible for the likelihood to decrease.
A thought that might be relevant is that the EM does not converge ... | Should the likelihood function be increasing in every step of the EM algorithm?
Perhaps not relevant in this case but note that if the E-step is estimated, with Monte Carlo methods or another approximation, it is possible for the likelihood to decrease.
A thought that might be re |
37,752 | Probability that 2 OH NFL teams go 31 weeks w/o wins on the same day | There is a big selection bias. It would make more sense to calculate the probability of any two teams going 31 weeks without both teams winning during the same week than just these two teams.
Your way of calculating seems better than your friend's. Assuming that probability of winning a game is 11/42 makes more sense t... | Probability that 2 OH NFL teams go 31 weeks w/o wins on the same day | There is a big selection bias. It would make more sense to calculate the probability of any two teams going 31 weeks without both teams winning during the same week than just these two teams.
Your way | Probability that 2 OH NFL teams go 31 weeks w/o wins on the same day
There is a big selection bias. It would make more sense to calculate the probability of any two teams going 31 weeks without both teams winning during the same week than just these two teams.
Your way of calculating seems better than your friend's. As... | Probability that 2 OH NFL teams go 31 weeks w/o wins on the same day
There is a big selection bias. It would make more sense to calculate the probability of any two teams going 31 weeks without both teams winning during the same week than just these two teams.
Your way |
37,753 | Comparison of frequency tables over time | Im not sure that I clearly understand the design of an experiment, but if more details are given, I will maybe re-answer your question. Mainly - how did CTRL and TRT differ from each other? Was number of students equal in each trial?
As I understand it, this should help.
First, you need to reorganize your data into arr... | Comparison of frequency tables over time | Im not sure that I clearly understand the design of an experiment, but if more details are given, I will maybe re-answer your question. Mainly - how did CTRL and TRT differ from each other? Was number | Comparison of frequency tables over time
Im not sure that I clearly understand the design of an experiment, but if more details are given, I will maybe re-answer your question. Mainly - how did CTRL and TRT differ from each other? Was number of students equal in each trial?
As I understand it, this should help.
First, ... | Comparison of frequency tables over time
Im not sure that I clearly understand the design of an experiment, but if more details are given, I will maybe re-answer your question. Mainly - how did CTRL and TRT differ from each other? Was number |
37,754 | How to predict the probability of a performance rank for a test taker on a future test based on previous test scores? | There is one big caveat and several smaller ones. First an approximate answer for Tom ranking first is $p=$ 0.6315 with 95% confidence intervals of 0.6306 to 0.6324. Note, the confidence intervals are from how precisely I determined an answer, not from what the variability of the probability actually is. Now the gory d... | How to predict the probability of a performance rank for a test taker on a future test based on prev | There is one big caveat and several smaller ones. First an approximate answer for Tom ranking first is $p=$ 0.6315 with 95% confidence intervals of 0.6306 to 0.6324. Note, the confidence intervals are | How to predict the probability of a performance rank for a test taker on a future test based on previous test scores?
There is one big caveat and several smaller ones. First an approximate answer for Tom ranking first is $p=$ 0.6315 with 95% confidence intervals of 0.6306 to 0.6324. Note, the confidence intervals are f... | How to predict the probability of a performance rank for a test taker on a future test based on prev
There is one big caveat and several smaller ones. First an approximate answer for Tom ranking first is $p=$ 0.6315 with 95% confidence intervals of 0.6306 to 0.6324. Note, the confidence intervals are |
37,755 | How to predict the probability of a performance rank for a test taker on a future test based on previous test scores? | A plan should be
consider a test as a 'match' between students
sort test in time order, and insert results in a rating system engine.
You can retrieve the probability that Tom will be ranked first in next test from ranking values.
If you're interested in use more than in development, rankade, our free ranking system... | How to predict the probability of a performance rank for a test taker on a future test based on prev | A plan should be
consider a test as a 'match' between students
sort test in time order, and insert results in a rating system engine.
You can retrieve the probability that Tom will be ranked first | How to predict the probability of a performance rank for a test taker on a future test based on previous test scores?
A plan should be
consider a test as a 'match' between students
sort test in time order, and insert results in a rating system engine.
You can retrieve the probability that Tom will be ranked first in... | How to predict the probability of a performance rank for a test taker on a future test based on prev
A plan should be
consider a test as a 'match' between students
sort test in time order, and insert results in a rating system engine.
You can retrieve the probability that Tom will be ranked first |
37,756 | How to examine interactions between factor and covariate in a mixed effects model? | For visualizing interaction terms, you may look at the sjPlot-package (see examples here).
Your function call would be
sjp.int(fit, type ="eff")
I'm not sure, however, if this meets your needs? | How to examine interactions between factor and covariate in a mixed effects model? | For visualizing interaction terms, you may look at the sjPlot-package (see examples here).
Your function call would be
sjp.int(fit, type ="eff")
I'm not sure, however, if this meets your needs? | How to examine interactions between factor and covariate in a mixed effects model?
For visualizing interaction terms, you may look at the sjPlot-package (see examples here).
Your function call would be
sjp.int(fit, type ="eff")
I'm not sure, however, if this meets your needs? | How to examine interactions between factor and covariate in a mixed effects model?
For visualizing interaction terms, you may look at the sjPlot-package (see examples here).
Your function call would be
sjp.int(fit, type ="eff")
I'm not sure, however, if this meets your needs? |
37,757 | How to examine interactions between factor and covariate in a mixed effects model? | I personally think that if you want to examine the true relationship between Y and the factors in your model after controlling for X you should be looking at the plotted adjusted rather than raw means computed from your favorite model.
For the purposes there are R packages such as lsmeans which are quite handy and use... | How to examine interactions between factor and covariate in a mixed effects model? | I personally think that if you want to examine the true relationship between Y and the factors in your model after controlling for X you should be looking at the plotted adjusted rather than raw means | How to examine interactions between factor and covariate in a mixed effects model?
I personally think that if you want to examine the true relationship between Y and the factors in your model after controlling for X you should be looking at the plotted adjusted rather than raw means computed from your favorite model.
... | How to examine interactions between factor and covariate in a mixed effects model?
I personally think that if you want to examine the true relationship between Y and the factors in your model after controlling for X you should be looking at the plotted adjusted rather than raw means |
37,758 | How to examine interactions between factor and covariate in a mixed effects model? | You can investigate the VIFs of your model. VIF stands for Variance Inflation Factor and is a way to measure co-linearity.
https://onlinecourses.science.psu.edu/stat501/node/347
There is a vif function in the car package for R. | How to examine interactions between factor and covariate in a mixed effects model? | You can investigate the VIFs of your model. VIF stands for Variance Inflation Factor and is a way to measure co-linearity.
https://onlinecourses.science.psu.edu/stat501/node/347
There is a vif functi | How to examine interactions between factor and covariate in a mixed effects model?
You can investigate the VIFs of your model. VIF stands for Variance Inflation Factor and is a way to measure co-linearity.
https://onlinecourses.science.psu.edu/stat501/node/347
There is a vif function in the car package for R. | How to examine interactions between factor and covariate in a mixed effects model?
You can investigate the VIFs of your model. VIF stands for Variance Inflation Factor and is a way to measure co-linearity.
https://onlinecourses.science.psu.edu/stat501/node/347
There is a vif functi |
37,759 | randomForest vs. cforest; Can I get partial dependence plots and percent variance explained in package party? | my package edarf will calculate partial dependence for predictors using cforest. you can get permutation using the varimp function in the party package as well.
yes cforest generates an ensemble of trees of the same form as ctree with random features selected at each node and subsampling (by default). control the param... | randomForest vs. cforest; Can I get partial dependence plots and percent variance explained in packa | my package edarf will calculate partial dependence for predictors using cforest. you can get permutation using the varimp function in the party package as well.
yes cforest generates an ensemble of tr | randomForest vs. cforest; Can I get partial dependence plots and percent variance explained in package party?
my package edarf will calculate partial dependence for predictors using cforest. you can get permutation using the varimp function in the party package as well.
yes cforest generates an ensemble of trees of the... | randomForest vs. cforest; Can I get partial dependence plots and percent variance explained in packa
my package edarf will calculate partial dependence for predictors using cforest. you can get permutation using the varimp function in the party package as well.
yes cforest generates an ensemble of tr |
37,760 | randomForest vs. cforest; Can I get partial dependence plots and percent variance explained in package party? | You can now make partial dependence plots for any learner in R by using the mlr package. Here the tutorial package that explains how to do that: tutorial | randomForest vs. cforest; Can I get partial dependence plots and percent variance explained in packa | You can now make partial dependence plots for any learner in R by using the mlr package. Here the tutorial package that explains how to do that: tutorial | randomForest vs. cforest; Can I get partial dependence plots and percent variance explained in package party?
You can now make partial dependence plots for any learner in R by using the mlr package. Here the tutorial package that explains how to do that: tutorial | randomForest vs. cforest; Can I get partial dependence plots and percent variance explained in packa
You can now make partial dependence plots for any learner in R by using the mlr package. Here the tutorial package that explains how to do that: tutorial |
37,761 | Measuring effects of categorical factors on binomial outcome with many groups | To circumvent the 200-players problem, you could fit whichever model you choose (logit, binomial...), without the player variable as such, but inside a discrete mixture framework. You'll have to process the data right (for instance you want to make sure that all stats of a single player are taken together, and you'll h... | Measuring effects of categorical factors on binomial outcome with many groups | To circumvent the 200-players problem, you could fit whichever model you choose (logit, binomial...), without the player variable as such, but inside a discrete mixture framework. You'll have to proce | Measuring effects of categorical factors on binomial outcome with many groups
To circumvent the 200-players problem, you could fit whichever model you choose (logit, binomial...), without the player variable as such, but inside a discrete mixture framework. You'll have to process the data right (for instance you want t... | Measuring effects of categorical factors on binomial outcome with many groups
To circumvent the 200-players problem, you could fit whichever model you choose (logit, binomial...), without the player variable as such, but inside a discrete mixture framework. You'll have to proce |
37,762 | Measuring effects of categorical factors on binomial outcome with many groups | I think you could fit a logistic regression model using player, ahead/behind, home/away, percentage success and number of shots taken under those conditions as possible covariates. Then difficulty with player is that you have over 200. I think that success percentage under specific conditions could serve as a substit... | Measuring effects of categorical factors on binomial outcome with many groups | I think you could fit a logistic regression model using player, ahead/behind, home/away, percentage success and number of shots taken under those conditions as possible covariates. Then difficulty wi | Measuring effects of categorical factors on binomial outcome with many groups
I think you could fit a logistic regression model using player, ahead/behind, home/away, percentage success and number of shots taken under those conditions as possible covariates. Then difficulty with player is that you have over 200. I th... | Measuring effects of categorical factors on binomial outcome with many groups
I think you could fit a logistic regression model using player, ahead/behind, home/away, percentage success and number of shots taken under those conditions as possible covariates. Then difficulty wi |
37,763 | Hidden Markov models and anomaly detection | According to this Wikipedia article, there are many inference benefits you gain from a HMM. The first inference is the ability to assign a probability to any observation sequence $\mathbf{Y} = (Y_1,\ldots, Y_N)$ by marginalizing over the set of all possible hidden state sequences $\mathbf{X} = (X_1,\ldots, X_N)$:
$P(\m... | Hidden Markov models and anomaly detection | According to this Wikipedia article, there are many inference benefits you gain from a HMM. The first inference is the ability to assign a probability to any observation sequence $\mathbf{Y} = (Y_1,\l | Hidden Markov models and anomaly detection
According to this Wikipedia article, there are many inference benefits you gain from a HMM. The first inference is the ability to assign a probability to any observation sequence $\mathbf{Y} = (Y_1,\ldots, Y_N)$ by marginalizing over the set of all possible hidden state sequen... | Hidden Markov models and anomaly detection
According to this Wikipedia article, there are many inference benefits you gain from a HMM. The first inference is the ability to assign a probability to any observation sequence $\mathbf{Y} = (Y_1,\l |
37,764 | Variable Selection One by One vs Simultaneously | Before going to the specifics of the method, first we need to understand the two classes of feature selection:
1. Univariate: where we consider the input features one by one.
2. Multivariate: where we consider a group of variables together.
In many cases univariate feature selection can produce good enough results and... | Variable Selection One by One vs Simultaneously | Before going to the specifics of the method, first we need to understand the two classes of feature selection:
1. Univariate: where we consider the input features one by one.
2. Multivariate: where w | Variable Selection One by One vs Simultaneously
Before going to the specifics of the method, first we need to understand the two classes of feature selection:
1. Univariate: where we consider the input features one by one.
2. Multivariate: where we consider a group of variables together.
In many cases univariate featu... | Variable Selection One by One vs Simultaneously
Before going to the specifics of the method, first we need to understand the two classes of feature selection:
1. Univariate: where we consider the input features one by one.
2. Multivariate: where w |
37,765 | Mean comparisons following multiple imputation | A recent paper by van Ginkel & Kroonenberg works out the details of pooling F-tests and other ANOVA results. The paper is:
van Ginkel, J. R., & Kroonenberg, P. M. (2014). Analysis of Variance of Multiply Imputed Data. Multivariate Behavioral Research, 49(1), 78-91.
and van Ginkel's website (http://www.socialsciences.l... | Mean comparisons following multiple imputation | A recent paper by van Ginkel & Kroonenberg works out the details of pooling F-tests and other ANOVA results. The paper is:
van Ginkel, J. R., & Kroonenberg, P. M. (2014). Analysis of Variance of Mult | Mean comparisons following multiple imputation
A recent paper by van Ginkel & Kroonenberg works out the details of pooling F-tests and other ANOVA results. The paper is:
van Ginkel, J. R., & Kroonenberg, P. M. (2014). Analysis of Variance of Multiply Imputed Data. Multivariate Behavioral Research, 49(1), 78-91.
and va... | Mean comparisons following multiple imputation
A recent paper by van Ginkel & Kroonenberg works out the details of pooling F-tests and other ANOVA results. The paper is:
van Ginkel, J. R., & Kroonenberg, P. M. (2014). Analysis of Variance of Mult |
37,766 | Modeling a spline over time -- design matrix and survey of approaches | I do agree with you that you may need to account for individual respondents errors terms through time particularly if you d not have result for all period for each respondent.
A way to do this is with the BayesX. It allows for spatial effects with splines where you can have time in one dimension and the covariate val... | Modeling a spline over time -- design matrix and survey of approaches | I do agree with you that you may need to account for individual respondents errors terms through time particularly if you d not have result for all period for each respondent.
A way to do this is wi | Modeling a spline over time -- design matrix and survey of approaches
I do agree with you that you may need to account for individual respondents errors terms through time particularly if you d not have result for all period for each respondent.
A way to do this is with the BayesX. It allows for spatial effects with ... | Modeling a spline over time -- design matrix and survey of approaches
I do agree with you that you may need to account for individual respondents errors terms through time particularly if you d not have result for all period for each respondent.
A way to do this is wi |
37,767 | Balanced repeated measures design | You don't need to specify a covariance structure and it is highly discouraged: If you choose the wrong structure, you might miss the targeted type-I-error. Instead, use the procedure described here. It is a generalization of ANOVA for unknown covariance matrices and even applicable if there are more repeated measures t... | Balanced repeated measures design | You don't need to specify a covariance structure and it is highly discouraged: If you choose the wrong structure, you might miss the targeted type-I-error. Instead, use the procedure described here. I | Balanced repeated measures design
You don't need to specify a covariance structure and it is highly discouraged: If you choose the wrong structure, you might miss the targeted type-I-error. Instead, use the procedure described here. It is a generalization of ANOVA for unknown covariance matrices and even applicable if ... | Balanced repeated measures design
You don't need to specify a covariance structure and it is highly discouraged: If you choose the wrong structure, you might miss the targeted type-I-error. Instead, use the procedure described here. I |
37,768 | Balanced repeated measures design | If you want to model the covariance structure simply as compound symmetric, the results from-mixed effect modeling and repeated-measures ANOVA should match if the data are balanced and not missing. If you want to model the covariance structure as something else (e.g., unstructured or autoregressive), then you need to u... | Balanced repeated measures design | If you want to model the covariance structure simply as compound symmetric, the results from-mixed effect modeling and repeated-measures ANOVA should match if the data are balanced and not missing. If | Balanced repeated measures design
If you want to model the covariance structure simply as compound symmetric, the results from-mixed effect modeling and repeated-measures ANOVA should match if the data are balanced and not missing. If you want to model the covariance structure as something else (e.g., unstructured or a... | Balanced repeated measures design
If you want to model the covariance structure simply as compound symmetric, the results from-mixed effect modeling and repeated-measures ANOVA should match if the data are balanced and not missing. If |
37,769 | Compute p-value in paired bootstrap | As far as I understand from looking at section 2, the authors seem to explain their rationale for the bootstrap test as follows-
"the $x_i$ were sampled from $x$, and so their average $\delta(x_i)$ won’t be zero like the null hypothesis demands; the average will instead be around $\delta(x)$... The solution is a re-ce... | Compute p-value in paired bootstrap | As far as I understand from looking at section 2, the authors seem to explain their rationale for the bootstrap test as follows-
"the $x_i$ were sampled from $x$, and so their average $\delta(x_i)$ w | Compute p-value in paired bootstrap
As far as I understand from looking at section 2, the authors seem to explain their rationale for the bootstrap test as follows-
"the $x_i$ were sampled from $x$, and so their average $\delta(x_i)$ won’t be zero like the null hypothesis demands; the average will instead be around $\... | Compute p-value in paired bootstrap
As far as I understand from looking at section 2, the authors seem to explain their rationale for the bootstrap test as follows-
"the $x_i$ were sampled from $x$, and so their average $\delta(x_i)$ w |
37,770 | How should I interpret the interaction term in a Cox proportional hazards model? | For models beyond the simplest (and an interaction makes it non-simple) I like to look at predictions instead of trying to interpret the coefficients directly. Does the software that you used to fit the model also do predictions for a given set of x and y? (many if not all do). You can then make predictions for patie... | How should I interpret the interaction term in a Cox proportional hazards model? | For models beyond the simplest (and an interaction makes it non-simple) I like to look at predictions instead of trying to interpret the coefficients directly. Does the software that you used to fit | How should I interpret the interaction term in a Cox proportional hazards model?
For models beyond the simplest (and an interaction makes it non-simple) I like to look at predictions instead of trying to interpret the coefficients directly. Does the software that you used to fit the model also do predictions for a giv... | How should I interpret the interaction term in a Cox proportional hazards model?
For models beyond the simplest (and an interaction makes it non-simple) I like to look at predictions instead of trying to interpret the coefficients directly. Does the software that you used to fit |
37,771 | How should I interpret the interaction term in a Cox proportional hazards model? | Did you find out the answers? I would like to know that, too. I think the interpretation is like this: With one-point increase in $Y$ and 10 point increase in $X$, the risk of death increases with 1.6% and this is significant. With keeping $X$ constant, increase in $Y$ decrease the risk (by 16.3%) and with keeping $Y$ ... | How should I interpret the interaction term in a Cox proportional hazards model? | Did you find out the answers? I would like to know that, too. I think the interpretation is like this: With one-point increase in $Y$ and 10 point increase in $X$, the risk of death increases with 1.6 | How should I interpret the interaction term in a Cox proportional hazards model?
Did you find out the answers? I would like to know that, too. I think the interpretation is like this: With one-point increase in $Y$ and 10 point increase in $X$, the risk of death increases with 1.6% and this is significant. With keeping... | How should I interpret the interaction term in a Cox proportional hazards model?
Did you find out the answers? I would like to know that, too. I think the interpretation is like this: With one-point increase in $Y$ and 10 point increase in $X$, the risk of death increases with 1.6 |
37,772 | Identifying differences among calibration curves: ANCOVA? | If you don't care about the intercept, I think you want this:
> model <- lm(response ~ -1 + conc + day:treat)
> anova(model)
Analysis of Variance Table
Response: response
Df Sum Sq Mean Sq F value Pr(>F)
conc 1 18.5545 18.5545 62.3458 9.909e-05 ***
day:treat 4 1.7860 0.4465 1.5003 0.2996... | Identifying differences among calibration curves: ANCOVA? | If you don't care about the intercept, I think you want this:
> model <- lm(response ~ -1 + conc + day:treat)
> anova(model)
Analysis of Variance Table
Response: response
Df Sum Sq Mean Sq | Identifying differences among calibration curves: ANCOVA?
If you don't care about the intercept, I think you want this:
> model <- lm(response ~ -1 + conc + day:treat)
> anova(model)
Analysis of Variance Table
Response: response
Df Sum Sq Mean Sq F value Pr(>F)
conc 1 18.5545 18.5545 62.3458 9.... | Identifying differences among calibration curves: ANCOVA?
If you don't care about the intercept, I think you want this:
> model <- lm(response ~ -1 + conc + day:treat)
> anova(model)
Analysis of Variance Table
Response: response
Df Sum Sq Mean Sq |
37,773 | Using MAD as a way of defining a threshold for significance testing | I doubt it. Most probably, the distribution of frequency terms is highly skewed. In such a case, using a threshold rule based on an assumption that the underlying data is drawn from a symmetrical distribution will give highly misleading thresholds (and as a result potentially results).
You could try to apply the thre... | Using MAD as a way of defining a threshold for significance testing | I doubt it. Most probably, the distribution of frequency terms is highly skewed. In such a case, using a threshold rule based on an assumption that the underlying data is drawn from a symmetrical dis | Using MAD as a way of defining a threshold for significance testing
I doubt it. Most probably, the distribution of frequency terms is highly skewed. In such a case, using a threshold rule based on an assumption that the underlying data is drawn from a symmetrical distribution will give highly misleading thresholds (an... | Using MAD as a way of defining a threshold for significance testing
I doubt it. Most probably, the distribution of frequency terms is highly skewed. In such a case, using a threshold rule based on an assumption that the underlying data is drawn from a symmetrical dis |
37,774 | Post processing random forests using regularised regression: what about bias? | I want to add some thoughts on the problem at hand, so that the discussion may roll on. However, I propose something else to think about, so others may comment on this.
When reading this post and the post in the highlighted link, we try to overcome the bias in the RF (possibly in the tails) and to correct biased outpu... | Post processing random forests using regularised regression: what about bias? | I want to add some thoughts on the problem at hand, so that the discussion may roll on. However, I propose something else to think about, so others may comment on this.
When reading this post and the | Post processing random forests using regularised regression: what about bias?
I want to add some thoughts on the problem at hand, so that the discussion may roll on. However, I propose something else to think about, so others may comment on this.
When reading this post and the post in the highlighted link, we try to o... | Post processing random forests using regularised regression: what about bias?
I want to add some thoughts on the problem at hand, so that the discussion may roll on. However, I propose something else to think about, so others may comment on this.
When reading this post and the |
37,775 | Post processing random forests using regularised regression: what about bias? | It is a decent question, asked a decade ago. The chat is long gone.
Question
Given a random forest:
That has bias in the outputs
That is decently trained with decent parameters and is otherwise non-pathological
has some similarity to this (link)
Find:
Elastic net regression, or other regularized regression, to cali... | Post processing random forests using regularised regression: what about bias? | It is a decent question, asked a decade ago. The chat is long gone.
Question
Given a random forest:
That has bias in the outputs
That is decently trained with decent parameters and is otherwise non- | Post processing random forests using regularised regression: what about bias?
It is a decent question, asked a decade ago. The chat is long gone.
Question
Given a random forest:
That has bias in the outputs
That is decently trained with decent parameters and is otherwise non-pathological
has some similarity to this (... | Post processing random forests using regularised regression: what about bias?
It is a decent question, asked a decade ago. The chat is long gone.
Question
Given a random forest:
That has bias in the outputs
That is decently trained with decent parameters and is otherwise non- |
37,776 | Time series with multiple subjects and multiple variables | As I mentioned in my note above, I would treat this as a regression problem. Here is a link to constructing, in R, the lag (and lead) variables from your data (R Head).
Included in the post is a brief introduction to using the resulting data in a regression model. You might also want to do a bit of background digging ... | Time series with multiple subjects and multiple variables | As I mentioned in my note above, I would treat this as a regression problem. Here is a link to constructing, in R, the lag (and lead) variables from your data (R Head).
Included in the post is a brief | Time series with multiple subjects and multiple variables
As I mentioned in my note above, I would treat this as a regression problem. Here is a link to constructing, in R, the lag (and lead) variables from your data (R Head).
Included in the post is a brief introduction to using the resulting data in a regression mode... | Time series with multiple subjects and multiple variables
As I mentioned in my note above, I would treat this as a regression problem. Here is a link to constructing, in R, the lag (and lead) variables from your data (R Head).
Included in the post is a brief |
37,777 | Time series with multiple subjects and multiple variables | You could create tables where the y1 is shifted by 0,1,2,3,4 weeks.
Then you run an analysis on them. For instance, you could make a neural network that tries to predict y1 from x. For some ideas, you can give Weka a spin.
Then, you have some measure of predicting y1 from x for each lag. Using this, you can find the la... | Time series with multiple subjects and multiple variables | You could create tables where the y1 is shifted by 0,1,2,3,4 weeks.
Then you run an analysis on them. For instance, you could make a neural network that tries to predict y1 from x. For some ideas, you | Time series with multiple subjects and multiple variables
You could create tables where the y1 is shifted by 0,1,2,3,4 weeks.
Then you run an analysis on them. For instance, you could make a neural network that tries to predict y1 from x. For some ideas, you can give Weka a spin.
Then, you have some measure of predicti... | Time series with multiple subjects and multiple variables
You could create tables where the y1 is shifted by 0,1,2,3,4 weeks.
Then you run an analysis on them. For instance, you could make a neural network that tries to predict y1 from x. For some ideas, you |
37,778 | How to adjust average rating for sample size on rating systems with more than two categories? | One way to cast your problem would be to treat it as a bayesian estimation problem.
Basically this means having a prior on your mean and update the mean based on each new observation over time.
A practical, yet theoretically disputable way to achieve this is to compute the mean as a function of the mean found in the co... | How to adjust average rating for sample size on rating systems with more than two categories? | One way to cast your problem would be to treat it as a bayesian estimation problem.
Basically this means having a prior on your mean and update the mean based on each new observation over time.
A prac | How to adjust average rating for sample size on rating systems with more than two categories?
One way to cast your problem would be to treat it as a bayesian estimation problem.
Basically this means having a prior on your mean and update the mean based on each new observation over time.
A practical, yet theoretically d... | How to adjust average rating for sample size on rating systems with more than two categories?
One way to cast your problem would be to treat it as a bayesian estimation problem.
Basically this means having a prior on your mean and update the mean based on each new observation over time.
A prac |
37,779 | How to adjust average rating for sample size on rating systems with more than two categories? | In the example you give, only one person has reviewed and given a score of 5/5. At this point, I would say you don't have enough information to give an informative estimate of the mean (or median). Possible scores are 1,2,3,4, or 5, so all you could say is that the mean average is somewhere between 1 and 5 and that one... | How to adjust average rating for sample size on rating systems with more than two categories? | In the example you give, only one person has reviewed and given a score of 5/5. At this point, I would say you don't have enough information to give an informative estimate of the mean (or median). Po | How to adjust average rating for sample size on rating systems with more than two categories?
In the example you give, only one person has reviewed and given a score of 5/5. At this point, I would say you don't have enough information to give an informative estimate of the mean (or median). Possible scores are 1,2,3,4,... | How to adjust average rating for sample size on rating systems with more than two categories?
In the example you give, only one person has reviewed and given a score of 5/5. At this point, I would say you don't have enough information to give an informative estimate of the mean (or median). Po |
37,780 | What is the confidence interval calculated in a spectral density periodogram in R? | You are on the right track by inspecting the source code. In addition to the spec.ci function you found, the plotting chunk for CI is given by
ci.text <- ""
conf.y <- max(x$spec)/conf.lim[2L]
conf.x <- max(x$freq) - x$bandwidth
lines(rep(conf.x, 2), conf.y * conf.lim, col = ci.col)
lines(conf.x + c(-0.5, 0.5) * x$bandw... | What is the confidence interval calculated in a spectral density periodogram in R? | You are on the right track by inspecting the source code. In addition to the spec.ci function you found, the plotting chunk for CI is given by
ci.text <- ""
conf.y <- max(x$spec)/conf.lim[2L]
conf.x < | What is the confidence interval calculated in a spectral density periodogram in R?
You are on the right track by inspecting the source code. In addition to the spec.ci function you found, the plotting chunk for CI is given by
ci.text <- ""
conf.y <- max(x$spec)/conf.lim[2L]
conf.x <- max(x$freq) - x$bandwidth
lines(rep... | What is the confidence interval calculated in a spectral density periodogram in R?
You are on the right track by inspecting the source code. In addition to the spec.ci function you found, the plotting chunk for CI is given by
ci.text <- ""
conf.y <- max(x$spec)/conf.lim[2L]
conf.x < |
37,781 | Technical variation versus real signal | The issue is you have various possible sources of randomness. Individual randomness (the normal error term in a linear regression); variation between your two measurements in each case; and variation from the particular units you've sampled. I think you probably want something like
model <- aov(outcome ~ condition + ... | Technical variation versus real signal | The issue is you have various possible sources of randomness. Individual randomness (the normal error term in a linear regression); variation between your two measurements in each case; and variation | Technical variation versus real signal
The issue is you have various possible sources of randomness. Individual randomness (the normal error term in a linear regression); variation between your two measurements in each case; and variation from the particular units you've sampled. I think you probably want something l... | Technical variation versus real signal
The issue is you have various possible sources of randomness. Individual randomness (the normal error term in a linear regression); variation between your two measurements in each case; and variation |
37,782 | If a factor variable is to be dropped in model selection, should all levels be dropped simultaneously? If so, why? [duplicate] | I'm really not sure what the answer would be in the absence of crossvalidation. But if we are crossvalidating, and we find that, say, one ethnic group out of 6 is substantially different from the others wrt Y, I can't seem to see anything wrong with using only that group's dummy variable in the followup equation. If m... | If a factor variable is to be dropped in model selection, should all levels be dropped simultaneousl | I'm really not sure what the answer would be in the absence of crossvalidation. But if we are crossvalidating, and we find that, say, one ethnic group out of 6 is substantially different from the othe | If a factor variable is to be dropped in model selection, should all levels be dropped simultaneously? If so, why? [duplicate]
I'm really not sure what the answer would be in the absence of crossvalidation. But if we are crossvalidating, and we find that, say, one ethnic group out of 6 is substantially different from t... | If a factor variable is to be dropped in model selection, should all levels be dropped simultaneousl
I'm really not sure what the answer would be in the absence of crossvalidation. But if we are crossvalidating, and we find that, say, one ethnic group out of 6 is substantially different from the othe |
37,783 | How can I compare my model to a technically invalid model? | One solution would be to use cross-validation methods. This might be a conceptually easy (and elegant) solution because the model you have differs significantly from the model to be compared to. AIC or BIC won't really work here because the functional forms of these two models are very different -- yours is nonlinear a... | How can I compare my model to a technically invalid model? | One solution would be to use cross-validation methods. This might be a conceptually easy (and elegant) solution because the model you have differs significantly from the model to be compared to. AIC o | How can I compare my model to a technically invalid model?
One solution would be to use cross-validation methods. This might be a conceptually easy (and elegant) solution because the model you have differs significantly from the model to be compared to. AIC or BIC won't really work here because the functional forms of ... | How can I compare my model to a technically invalid model?
One solution would be to use cross-validation methods. This might be a conceptually easy (and elegant) solution because the model you have differs significantly from the model to be compared to. AIC o |
37,784 | Covariate modeling with a within-subject factor | I would suggest using random model in this case. In my opinion to allow for within and across subjects variability it is better to consider it as a hierarchical modelling. The first level allows for within subjects variation and the second level allows for across subjects variation.
To clarify, in the first level you t... | Covariate modeling with a within-subject factor | I would suggest using random model in this case. In my opinion to allow for within and across subjects variability it is better to consider it as a hierarchical modelling. The first level allows for w | Covariate modeling with a within-subject factor
I would suggest using random model in this case. In my opinion to allow for within and across subjects variability it is better to consider it as a hierarchical modelling. The first level allows for within subjects variation and the second level allows for across subjects... | Covariate modeling with a within-subject factor
I would suggest using random model in this case. In my opinion to allow for within and across subjects variability it is better to consider it as a hierarchical modelling. The first level allows for w |
37,785 | How to calculate sample size for comparing the area under the curve of two models? | I could not find an R-package that would solve the problem. But I can remember reading the book "Statistical Methods in Diagnostic Medicine" by Zhou, Obuchowski and McClish
(Amazon Link). They give a method (too long to reproduce it here for now) for determining the sample size and refer to 2 publications:
Obuchowski N... | How to calculate sample size for comparing the area under the curve of two models? | I could not find an R-package that would solve the problem. But I can remember reading the book "Statistical Methods in Diagnostic Medicine" by Zhou, Obuchowski and McClish
(Amazon Link). They give a | How to calculate sample size for comparing the area under the curve of two models?
I could not find an R-package that would solve the problem. But I can remember reading the book "Statistical Methods in Diagnostic Medicine" by Zhou, Obuchowski and McClish
(Amazon Link). They give a method (too long to reproduce it here... | How to calculate sample size for comparing the area under the curve of two models?
I could not find an R-package that would solve the problem. But I can remember reading the book "Statistical Methods in Diagnostic Medicine" by Zhou, Obuchowski and McClish
(Amazon Link). They give a |
37,786 | How to calculate sample size for comparing the area under the curve of two models? | There is a just released R Package called pROC which does what you want using the function power.roc.test.
Documentation of the package:
https://cran.r-project.org/web/packages/pROC/pROC.pdf
I Hope it helps | How to calculate sample size for comparing the area under the curve of two models? | There is a just released R Package called pROC which does what you want using the function power.roc.test.
Documentation of the package:
https://cran.r-project.org/web/packages/pROC/pROC.pdf
I Hope it | How to calculate sample size for comparing the area under the curve of two models?
There is a just released R Package called pROC which does what you want using the function power.roc.test.
Documentation of the package:
https://cran.r-project.org/web/packages/pROC/pROC.pdf
I Hope it helps | How to calculate sample size for comparing the area under the curve of two models?
There is a just released R Package called pROC which does what you want using the function power.roc.test.
Documentation of the package:
https://cran.r-project.org/web/packages/pROC/pROC.pdf
I Hope it |
37,787 | How to predict future reservations when data for the current day is incomplete? | Ratio estimates just don't work. For example if it takes 1 hour to complete three innings at a baseball game you can rest assured that the next 6 innings is going to take a lot longer than 2 hours to complete.In order to predict tomorrow given partial information for today and full information for the past NOB days, I ... | How to predict future reservations when data for the current day is incomplete? | Ratio estimates just don't work. For example if it takes 1 hour to complete three innings at a baseball game you can rest assured that the next 6 innings is going to take a lot longer than 2 hours to | How to predict future reservations when data for the current day is incomplete?
Ratio estimates just don't work. For example if it takes 1 hour to complete three innings at a baseball game you can rest assured that the next 6 innings is going to take a lot longer than 2 hours to complete.In order to predict tomorrow gi... | How to predict future reservations when data for the current day is incomplete?
Ratio estimates just don't work. For example if it takes 1 hour to complete three innings at a baseball game you can rest assured that the next 6 innings is going to take a lot longer than 2 hours to |
37,788 | How to interpret coefficients produced by the sem function in R? | sem gives direct effects only. To get total as well as indirect effects use the functions given by John Fox. | How to interpret coefficients produced by the sem function in R? | sem gives direct effects only. To get total as well as indirect effects use the functions given by John Fox. | How to interpret coefficients produced by the sem function in R?
sem gives direct effects only. To get total as well as indirect effects use the functions given by John Fox. | How to interpret coefficients produced by the sem function in R?
sem gives direct effects only. To get total as well as indirect effects use the functions given by John Fox. |
37,789 | Multicategory choice model with given categories | Did you read this?
http://www.jstor.org/pss/30038862
Edwards and Allenby seem to have the same basic setup as you, a multivariate probit, which you can find the code in the bayesm package.
It seems you should be able to evaluate the dependency by a test of if the probits are independent in the different scenarios by a ... | Multicategory choice model with given categories | Did you read this?
http://www.jstor.org/pss/30038862
Edwards and Allenby seem to have the same basic setup as you, a multivariate probit, which you can find the code in the bayesm package.
It seems yo | Multicategory choice model with given categories
Did you read this?
http://www.jstor.org/pss/30038862
Edwards and Allenby seem to have the same basic setup as you, a multivariate probit, which you can find the code in the bayesm package.
It seems you should be able to evaluate the dependency by a test of if the probits... | Multicategory choice model with given categories
Did you read this?
http://www.jstor.org/pss/30038862
Edwards and Allenby seem to have the same basic setup as you, a multivariate probit, which you can find the code in the bayesm package.
It seems yo |
37,790 | How to analyse repeated measure ANOVA with three or more conditions presented in randomised order? | Repeated measures is kind of an overloaded term. To some people it refers to a particular statistical analysis method; to others it refers to the structure of the design.
This is a variant on a three period, three treatment crossover design.
It is a variant because usually in a crossover design you randomize subjects... | How to analyse repeated measure ANOVA with three or more conditions presented in randomised order? | Repeated measures is kind of an overloaded term. To some people it refers to a particular statistical analysis method; to others it refers to the structure of the design.
This is a variant on a thre | How to analyse repeated measure ANOVA with three or more conditions presented in randomised order?
Repeated measures is kind of an overloaded term. To some people it refers to a particular statistical analysis method; to others it refers to the structure of the design.
This is a variant on a three period, three treat... | How to analyse repeated measure ANOVA with three or more conditions presented in randomised order?
Repeated measures is kind of an overloaded term. To some people it refers to a particular statistical analysis method; to others it refers to the structure of the design.
This is a variant on a thre |
37,791 | Is there a name for the high sensitivity of frequency of extreme data points to the mean of a normal distribution? | This is not an answer to the question, but may be of interest. The question gave three ratios of number of people in the mean=110 and mean=100 populations having IQ above a given threshold (ratio(IQ=150)≈10, ratio(120)≈3, ratio(110)≈2). The R code below plots the ratio as a function of IQ.
IQ = seq(0, 200, length.out=1... | Is there a name for the high sensitivity of frequency of extreme data points to the mean of a normal | This is not an answer to the question, but may be of interest. The question gave three ratios of number of people in the mean=110 and mean=100 populations having IQ above a given threshold (ratio(IQ=1 | Is there a name for the high sensitivity of frequency of extreme data points to the mean of a normal distribution?
This is not an answer to the question, but may be of interest. The question gave three ratios of number of people in the mean=110 and mean=100 populations having IQ above a given threshold (ratio(IQ=150)≈1... | Is there a name for the high sensitivity of frequency of extreme data points to the mean of a normal
This is not an answer to the question, but may be of interest. The question gave three ratios of number of people in the mean=110 and mean=100 populations having IQ above a given threshold (ratio(IQ=1 |
37,792 | How to model time-series temperature data at multiple sites as a function of data at one site? | You may want to examine the GAM package in R, as it can be adapted to do some (or all) of what you are looking for. The original paper (Hastie & Tibshirani, 1986) is available via OpenAccess if you're up for reading it.
Essentially, you model a single dependent variable as being an additive combination of 'smooth' pred... | How to model time-series temperature data at multiple sites as a function of data at one site? | You may want to examine the GAM package in R, as it can be adapted to do some (or all) of what you are looking for. The original paper (Hastie & Tibshirani, 1986) is available via OpenAccess if you're | How to model time-series temperature data at multiple sites as a function of data at one site?
You may want to examine the GAM package in R, as it can be adapted to do some (or all) of what you are looking for. The original paper (Hastie & Tibshirani, 1986) is available via OpenAccess if you're up for reading it.
Essen... | How to model time-series temperature data at multiple sites as a function of data at one site?
You may want to examine the GAM package in R, as it can be adapted to do some (or all) of what you are looking for. The original paper (Hastie & Tibshirani, 1986) is available via OpenAccess if you're |
37,793 | How to model time-series temperature data at multiple sites as a function of data at one site? | Whether or not you wish to forecast or not has nothing whatsoever to do with correct time series analysis. Time series methods can develop a robust model which can be used simply to characterize the relationship between a dependent series and a set of user-suggested inputs (a.k.a. user-specified predictor series) and e... | How to model time-series temperature data at multiple sites as a function of data at one site? | Whether or not you wish to forecast or not has nothing whatsoever to do with correct time series analysis. Time series methods can develop a robust model which can be used simply to characterize the r | How to model time-series temperature data at multiple sites as a function of data at one site?
Whether or not you wish to forecast or not has nothing whatsoever to do with correct time series analysis. Time series methods can develop a robust model which can be used simply to characterize the relationship between a dep... | How to model time-series temperature data at multiple sites as a function of data at one site?
Whether or not you wish to forecast or not has nothing whatsoever to do with correct time series analysis. Time series methods can develop a robust model which can be used simply to characterize the r |
37,794 | What is the right name for the variant of the Kolmogorov-Smirnov statistic that retains the sign of the difference? | It looks like a variant of Kuiper's test to me, although Kuiper's V = D+ + D− ≠ D'. | What is the right name for the variant of the Kolmogorov-Smirnov statistic that retains the sign of | It looks like a variant of Kuiper's test to me, although Kuiper's V = D+ + D− ≠ D'. | What is the right name for the variant of the Kolmogorov-Smirnov statistic that retains the sign of the difference?
It looks like a variant of Kuiper's test to me, although Kuiper's V = D+ + D− ≠ D'. | What is the right name for the variant of the Kolmogorov-Smirnov statistic that retains the sign of
It looks like a variant of Kuiper's test to me, although Kuiper's V = D+ + D− ≠ D'. |
37,795 | Self-organizing maps: fuzzy input? | There are a few papers which propose fuzzy SOM.
Petri Vuorimaa, "Fuzzy self-organizing map", Fuzzy Sets and Systems
Volume 66, Issue 2, 9 September 1994, Pages 223-231.
Kohonen's Self-Organizing Map is one of the best-known neural network models. In this paper, we introduce a fuzzy version of the model called: Fuzzy S... | Self-organizing maps: fuzzy input? | There are a few papers which propose fuzzy SOM.
Petri Vuorimaa, "Fuzzy self-organizing map", Fuzzy Sets and Systems
Volume 66, Issue 2, 9 September 1994, Pages 223-231.
Kohonen's Self-Organizing Map | Self-organizing maps: fuzzy input?
There are a few papers which propose fuzzy SOM.
Petri Vuorimaa, "Fuzzy self-organizing map", Fuzzy Sets and Systems
Volume 66, Issue 2, 9 September 1994, Pages 223-231.
Kohonen's Self-Organizing Map is one of the best-known neural network models. In this paper, we introduce a fuzzy v... | Self-organizing maps: fuzzy input?
There are a few papers which propose fuzzy SOM.
Petri Vuorimaa, "Fuzzy self-organizing map", Fuzzy Sets and Systems
Volume 66, Issue 2, 9 September 1994, Pages 223-231.
Kohonen's Self-Organizing Map |
37,796 | Reliability of mean of standard deviations | If you want to test whether the variances of several machines deviates from the other variances combining them into average will not help you. The problem is that these differing variances will skew your average. To test whether there are different variances you can use Bartlet's test. It is sensitive to normality, but... | Reliability of mean of standard deviations | If you want to test whether the variances of several machines deviates from the other variances combining them into average will not help you. The problem is that these differing variances will skew y | Reliability of mean of standard deviations
If you want to test whether the variances of several machines deviates from the other variances combining them into average will not help you. The problem is that these differing variances will skew your average. To test whether there are different variances you can use Bartle... | Reliability of mean of standard deviations
If you want to test whether the variances of several machines deviates from the other variances combining them into average will not help you. The problem is that these differing variances will skew y |
37,797 | Explaining conditioning number in statistics to non-statisticians | Answered (and seemingly accepted) in the comments by Demetri Pananos:
Conditioning numbers were explained to me like a nightmare shower nob. You ever take a shower in which the smallest twist of the nob dramatically changes the temperature? That is what conditioning numbers are like. | Explaining conditioning number in statistics to non-statisticians | Answered (and seemingly accepted) in the comments by Demetri Pananos:
Conditioning numbers were explained to me like a nightmare shower nob. You ever take a shower in which the smallest twist of the | Explaining conditioning number in statistics to non-statisticians
Answered (and seemingly accepted) in the comments by Demetri Pananos:
Conditioning numbers were explained to me like a nightmare shower nob. You ever take a shower in which the smallest twist of the nob dramatically changes the temperature? That is what... | Explaining conditioning number in statistics to non-statisticians
Answered (and seemingly accepted) in the comments by Demetri Pananos:
Conditioning numbers were explained to me like a nightmare shower nob. You ever take a shower in which the smallest twist of the |
37,798 | Interactions - using ratio of variables | Given the basic arithmetic:
$$\frac{x}{y} = x \times \frac{1}{y}$$
this does not really matter. So growth rate in is: $\text{size} \times (1 / \text{age}) $ and you already have this effect in your interaction. If it makes interpretation easier you can always work with reverse age rather than age as independent variabl... | Interactions - using ratio of variables | Given the basic arithmetic:
$$\frac{x}{y} = x \times \frac{1}{y}$$
this does not really matter. So growth rate in is: $\text{size} \times (1 / \text{age}) $ and you already have this effect in your in | Interactions - using ratio of variables
Given the basic arithmetic:
$$\frac{x}{y} = x \times \frac{1}{y}$$
this does not really matter. So growth rate in is: $\text{size} \times (1 / \text{age}) $ and you already have this effect in your interaction. If it makes interpretation easier you can always work with reverse ag... | Interactions - using ratio of variables
Given the basic arithmetic:
$$\frac{x}{y} = x \times \frac{1}{y}$$
this does not really matter. So growth rate in is: $\text{size} \times (1 / \text{age}) $ and you already have this effect in your in |
37,799 | Interactions - using ratio of variables | As size = age * growth rate, you could also see size as interaction between age and growth rate. | Interactions - using ratio of variables | As size = age * growth rate, you could also see size as interaction between age and growth rate. | Interactions - using ratio of variables
As size = age * growth rate, you could also see size as interaction between age and growth rate. | Interactions - using ratio of variables
As size = age * growth rate, you could also see size as interaction between age and growth rate. |
37,800 | How to test the increase of proportions | One is to use a Cochran Armitage trend test. There is an implementation in R. I suppose you are interested in event vs non-event, so you need to flip your matrix:
library(DescTools)
m <- matrix(c(71,248,419,796,285,288),ncol=3)
CochranArmitageTest(m)
Cochran-Armitage test for trend
data: m
Z = 8.5195, dim = 3, p-val... | How to test the increase of proportions | One is to use a Cochran Armitage trend test. There is an implementation in R. I suppose you are interested in event vs non-event, so you need to flip your matrix:
library(DescTools)
m <- matrix(c(71,2 | How to test the increase of proportions
One is to use a Cochran Armitage trend test. There is an implementation in R. I suppose you are interested in event vs non-event, so you need to flip your matrix:
library(DescTools)
m <- matrix(c(71,248,419,796,285,288),ncol=3)
CochranArmitageTest(m)
Cochran-Armitage test for tr... | How to test the increase of proportions
One is to use a Cochran Armitage trend test. There is an implementation in R. I suppose you are interested in event vs non-event, so you need to flip your matrix:
library(DescTools)
m <- matrix(c(71,2 |
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