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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43,501 | Negative variance from inverse Hessian matrix | The variance covariance matrix can be aproximated by the inverse of the negative Hessian H (matrix of second order partial derivatives). May be the reason is that you are approximating using the inverse $H$, when it should be the inverse of the negative H. | Negative variance from inverse Hessian matrix | The variance covariance matrix can be aproximated by the inverse of the negative Hessian H (matrix of second order partial derivatives). May be the reason is that you are approximating using the inver | Negative variance from inverse Hessian matrix
The variance covariance matrix can be aproximated by the inverse of the negative Hessian H (matrix of second order partial derivatives). May be the reason is that you are approximating using the inverse $H$, when it should be the inverse of the negative H. | Negative variance from inverse Hessian matrix
The variance covariance matrix can be aproximated by the inverse of the negative Hessian H (matrix of second order partial derivatives). May be the reason is that you are approximating using the inver |
43,502 | Forward Filtering Backwards Sampling (FFBS) and Look-Ahead Bias | This question is a little old, but maybe it is still relevant.
As far as I understand your question, if you have $T$ data available, and are trying to predict observations in the future, surely you're "cheating" if you're using the whole data to "predict" $y_{t}$ with $t<T$. Indeed, what you're doing there is smoothing... | Forward Filtering Backwards Sampling (FFBS) and Look-Ahead Bias | This question is a little old, but maybe it is still relevant.
As far as I understand your question, if you have $T$ data available, and are trying to predict observations in the future, surely you're | Forward Filtering Backwards Sampling (FFBS) and Look-Ahead Bias
This question is a little old, but maybe it is still relevant.
As far as I understand your question, if you have $T$ data available, and are trying to predict observations in the future, surely you're "cheating" if you're using the whole data to "predict" ... | Forward Filtering Backwards Sampling (FFBS) and Look-Ahead Bias
This question is a little old, but maybe it is still relevant.
As far as I understand your question, if you have $T$ data available, and are trying to predict observations in the future, surely you're |
43,503 | Forward Filtering Backwards Sampling (FFBS) and Look-Ahead Bias | If the parameters you are estimating do not vary over time, you may split the observed time series $y_{1:T}$ in two series, $y_{1:k}$ and $y_{k+1:T}$. Then you would apply MCMC on $y_{1:k}$ in order to sample from the posterior distribution of your parameters. After that, you could fix your parameters at some estimate ... | Forward Filtering Backwards Sampling (FFBS) and Look-Ahead Bias | If the parameters you are estimating do not vary over time, you may split the observed time series $y_{1:T}$ in two series, $y_{1:k}$ and $y_{k+1:T}$. Then you would apply MCMC on $y_{1:k}$ in order t | Forward Filtering Backwards Sampling (FFBS) and Look-Ahead Bias
If the parameters you are estimating do not vary over time, you may split the observed time series $y_{1:T}$ in two series, $y_{1:k}$ and $y_{k+1:T}$. Then you would apply MCMC on $y_{1:k}$ in order to sample from the posterior distribution of your paramet... | Forward Filtering Backwards Sampling (FFBS) and Look-Ahead Bias
If the parameters you are estimating do not vary over time, you may split the observed time series $y_{1:T}$ in two series, $y_{1:k}$ and $y_{k+1:T}$. Then you would apply MCMC on $y_{1:k}$ in order t |
43,504 | How to estimate the point of divergence between two continuous time survival curves? | There is some ambiguity in what the OP means by the "pair of survival curves" obtained from each of the "N samples."
If the "survival curves" are changes in the value of some continuous variable as a function of time from a maximum initial value down to zero for two cases or two groups within each of the N samples, the... | How to estimate the point of divergence between two continuous time survival curves? | There is some ambiguity in what the OP means by the "pair of survival curves" obtained from each of the "N samples."
If the "survival curves" are changes in the value of some continuous variable as a | How to estimate the point of divergence between two continuous time survival curves?
There is some ambiguity in what the OP means by the "pair of survival curves" obtained from each of the "N samples."
If the "survival curves" are changes in the value of some continuous variable as a function of time from a maximum ini... | How to estimate the point of divergence between two continuous time survival curves?
There is some ambiguity in what the OP means by the "pair of survival curves" obtained from each of the "N samples."
If the "survival curves" are changes in the value of some continuous variable as a |
43,505 | How to estimate the point of divergence between two continuous time survival curves? | One method that might be useful is change point detection.
https://en.wikipedia.org/wiki/Change_detection
There are many variants, but in general you partition the data into two sets and compute a test statistic, such as the Kolmogov-Smirnov, to determine whether the two distributions are the same. If null hypothesis ... | How to estimate the point of divergence between two continuous time survival curves? | One method that might be useful is change point detection.
https://en.wikipedia.org/wiki/Change_detection
There are many variants, but in general you partition the data into two sets and compute a tes | How to estimate the point of divergence between two continuous time survival curves?
One method that might be useful is change point detection.
https://en.wikipedia.org/wiki/Change_detection
There are many variants, but in general you partition the data into two sets and compute a test statistic, such as the Kolmogov-S... | How to estimate the point of divergence between two continuous time survival curves?
One method that might be useful is change point detection.
https://en.wikipedia.org/wiki/Change_detection
There are many variants, but in general you partition the data into two sets and compute a tes |
43,506 | Good (2d) visualization of a mixture model clustering | Try visualization methods, such as surface plots and other techniques for high-dimensional data visualization, described in the paper "mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation" by Chris Fraley, Adrian E. Raftery, T. Brendan Murphy and Luca Scrucc... | Good (2d) visualization of a mixture model clustering | Try visualization methods, such as surface plots and other techniques for high-dimensional data visualization, described in the paper "mclust Version 4 for R: Normal Mixture Modeling for Model-Based C | Good (2d) visualization of a mixture model clustering
Try visualization methods, such as surface plots and other techniques for high-dimensional data visualization, described in the paper "mclust Version 4 for R: Normal Mixture Modeling for Model-Based Clustering, Classification, and Density Estimation" by Chris Fraley... | Good (2d) visualization of a mixture model clustering
Try visualization methods, such as surface plots and other techniques for high-dimensional data visualization, described in the paper "mclust Version 4 for R: Normal Mixture Modeling for Model-Based C |
43,507 | Understanding vec2var conversion in R | The vec2var() function will provide an object of class vec2var.
You cannot convert it further to the class varest for standard VAR, but there are still many methods that will work on it, check it with:
methods(class = "vec2var")
Will return:
[1] fevd fitted irf logLik Phi plot predict pr... | Understanding vec2var conversion in R | The vec2var() function will provide an object of class vec2var.
You cannot convert it further to the class varest for standard VAR, but there are still many methods that will work on it, check it wit | Understanding vec2var conversion in R
The vec2var() function will provide an object of class vec2var.
You cannot convert it further to the class varest for standard VAR, but there are still many methods that will work on it, check it with:
methods(class = "vec2var")
Will return:
[1] fevd fitted irf log... | Understanding vec2var conversion in R
The vec2var() function will provide an object of class vec2var.
You cannot convert it further to the class varest for standard VAR, but there are still many methods that will work on it, check it wit |
43,508 | Zero-inflation on steroids: choose among Poisson, negative binomial and zero-inflated regressions | You have 13 observations that are non-zero and only 3 observations that are greater than 1. Hence, I believe the best thing you can do is to use a binary regression only (0 vs > 0). Possibly it could help to add some form of regularization for that as well, e.g. bias reduction (see package brglm), because non-zeros are... | Zero-inflation on steroids: choose among Poisson, negative binomial and zero-inflated regressions | You have 13 observations that are non-zero and only 3 observations that are greater than 1. Hence, I believe the best thing you can do is to use a binary regression only (0 vs > 0). Possibly it could | Zero-inflation on steroids: choose among Poisson, negative binomial and zero-inflated regressions
You have 13 observations that are non-zero and only 3 observations that are greater than 1. Hence, I believe the best thing you can do is to use a binary regression only (0 vs > 0). Possibly it could help to add some form ... | Zero-inflation on steroids: choose among Poisson, negative binomial and zero-inflated regressions
You have 13 observations that are non-zero and only 3 observations that are greater than 1. Hence, I believe the best thing you can do is to use a binary regression only (0 vs > 0). Possibly it could |
43,509 | Are Restricted Boltzmann Machines better than Stacked Auto encoders and why? | Auto-encoders typically feature many hidden layers. This causes a variety of problems for the common backpropagation-style training methods, because the backpropagated errors become very small in the first few layers.
A solution is to do pretraining, e.g. use initial weights that approximate the final solution. One pre... | Are Restricted Boltzmann Machines better than Stacked Auto encoders and why? | Auto-encoders typically feature many hidden layers. This causes a variety of problems for the common backpropagation-style training methods, because the backpropagated errors become very small in the | Are Restricted Boltzmann Machines better than Stacked Auto encoders and why?
Auto-encoders typically feature many hidden layers. This causes a variety of problems for the common backpropagation-style training methods, because the backpropagated errors become very small in the first few layers.
A solution is to do pretr... | Are Restricted Boltzmann Machines better than Stacked Auto encoders and why?
Auto-encoders typically feature many hidden layers. This causes a variety of problems for the common backpropagation-style training methods, because the backpropagated errors become very small in the |
43,510 | Constants in a DLM Model R | If you set the last three terms of m0 equal to zero and the variances in C0 and W equal to 10^-7, you don't give $\mu$ much of a chance to take off from zero. Not surprising that they come out as zero. | Constants in a DLM Model R | If you set the last three terms of m0 equal to zero and the variances in C0 and W equal to 10^-7, you don't give $\mu$ much of a chance to take off from zero. Not surprising that they come out as zero | Constants in a DLM Model R
If you set the last three terms of m0 equal to zero and the variances in C0 and W equal to 10^-7, you don't give $\mu$ much of a chance to take off from zero. Not surprising that they come out as zero. | Constants in a DLM Model R
If you set the last three terms of m0 equal to zero and the variances in C0 and W equal to 10^-7, you don't give $\mu$ much of a chance to take off from zero. Not surprising that they come out as zero |
43,511 | Why can we use entropy to measure the quality of a language model? | (For more info, please check here: https://stackoverflow.com/questions/22933412/why-can-we-use-entropy-to-measure-the-quality-of-language-model)
After I re-digested the mentioned NLP book. I think I can explain it now.
What I calculated is actually the entropy of the language model distribution. It cannot be used to ev... | Why can we use entropy to measure the quality of a language model? | (For more info, please check here: https://stackoverflow.com/questions/22933412/why-can-we-use-entropy-to-measure-the-quality-of-language-model)
After I re-digested the mentioned NLP book. I think I c | Why can we use entropy to measure the quality of a language model?
(For more info, please check here: https://stackoverflow.com/questions/22933412/why-can-we-use-entropy-to-measure-the-quality-of-language-model)
After I re-digested the mentioned NLP book. I think I can explain it now.
What I calculated is actually the ... | Why can we use entropy to measure the quality of a language model?
(For more info, please check here: https://stackoverflow.com/questions/22933412/why-can-we-use-entropy-to-measure-the-quality-of-language-model)
After I re-digested the mentioned NLP book. I think I c |
43,512 | Linearly dependent features | A little late but...
There's a measure called Pearson correlation that can be used to find linear correlation (dependence) between two variables X and Y. In short it is the covariance of the two variables divided by the product of their standard deviations:
The result is a value between +1 and −1 inclusive, where 1 i... | Linearly dependent features | A little late but...
There's a measure called Pearson correlation that can be used to find linear correlation (dependence) between two variables X and Y. In short it is the covariance of the two vari | Linearly dependent features
A little late but...
There's a measure called Pearson correlation that can be used to find linear correlation (dependence) between two variables X and Y. In short it is the covariance of the two variables divided by the product of their standard deviations:
The result is a value between +1... | Linearly dependent features
A little late but...
There's a measure called Pearson correlation that can be used to find linear correlation (dependence) between two variables X and Y. In short it is the covariance of the two vari |
43,513 | Linearly dependent features | One approach would be to use an incomplete Cholesky factorisation, I have some MATLAB code here, see the paper by Fine and Scheinberg mentioned on that page for details. | Linearly dependent features | One approach would be to use an incomplete Cholesky factorisation, I have some MATLAB code here, see the paper by Fine and Scheinberg mentioned on that page for details. | Linearly dependent features
One approach would be to use an incomplete Cholesky factorisation, I have some MATLAB code here, see the paper by Fine and Scheinberg mentioned on that page for details. | Linearly dependent features
One approach would be to use an incomplete Cholesky factorisation, I have some MATLAB code here, see the paper by Fine and Scheinberg mentioned on that page for details. |
43,514 | Linearly dependent features | Yes, rank roughly tells you how many column-vectors (features) are independent.
No, you can't remove arbitrary columns. You can try removing random columns and calculating the rank of a result — you'll see different numbers. You need to remove only those features that are dependent.
Or, suppose that you generated data ... | Linearly dependent features | Yes, rank roughly tells you how many column-vectors (features) are independent.
No, you can't remove arbitrary columns. You can try removing random columns and calculating the rank of a result — you'l | Linearly dependent features
Yes, rank roughly tells you how many column-vectors (features) are independent.
No, you can't remove arbitrary columns. You can try removing random columns and calculating the rank of a result — you'll see different numbers. You need to remove only those features that are dependent.
Or, supp... | Linearly dependent features
Yes, rank roughly tells you how many column-vectors (features) are independent.
No, you can't remove arbitrary columns. You can try removing random columns and calculating the rank of a result — you'l |
43,515 | Bonferroni correction for two different tests on the same dataset | In general it shouldn't matter how the p-values were calculated (ie which particular test statistic they came from). A $p < .05$ type decision still has a $5\%$ chance of a false positive, so if you did three hypothesis tests you should correct for three hypothesis tests. | Bonferroni correction for two different tests on the same dataset | In general it shouldn't matter how the p-values were calculated (ie which particular test statistic they came from). A $p < .05$ type decision still has a $5\%$ chance of a false positive, so if you d | Bonferroni correction for two different tests on the same dataset
In general it shouldn't matter how the p-values were calculated (ie which particular test statistic they came from). A $p < .05$ type decision still has a $5\%$ chance of a false positive, so if you did three hypothesis tests you should correct for three... | Bonferroni correction for two different tests on the same dataset
In general it shouldn't matter how the p-values were calculated (ie which particular test statistic they came from). A $p < .05$ type decision still has a $5\%$ chance of a false positive, so if you d |
43,516 | How do I estimate the average size of objects | This is the kind of problem treated in finite population sampling theory, as presented in the book http://www.amazon.com/Finite-Population-Sampling-Inference-Prediction/dp/0471293415/ref=sr_1_1?s=books&ie=UTF8&qid=1401276486&sr=1-1&keywords=finite+population+sampling+theory
(and many others).
First of all, you will wan... | How do I estimate the average size of objects | This is the kind of problem treated in finite population sampling theory, as presented in the book http://www.amazon.com/Finite-Population-Sampling-Inference-Prediction/dp/0471293415/ref=sr_1_1?s=book | How do I estimate the average size of objects
This is the kind of problem treated in finite population sampling theory, as presented in the book http://www.amazon.com/Finite-Population-Sampling-Inference-Prediction/dp/0471293415/ref=sr_1_1?s=books&ie=UTF8&qid=1401276486&sr=1-1&keywords=finite+population+sampling+theory... | How do I estimate the average size of objects
This is the kind of problem treated in finite population sampling theory, as presented in the book http://www.amazon.com/Finite-Population-Sampling-Inference-Prediction/dp/0471293415/ref=sr_1_1?s=book |
43,517 | How do I estimate the average size of objects | An interval for the mean will
(i) be probabilistic - that is, with random sampling, you could compute a mean that's no more than 20% off 99% of the time (or some other percentage) -- but not 100% unless your sample is your population.
(ii) depend on the standard deviation of the number of pages; since this will be unk... | How do I estimate the average size of objects | An interval for the mean will
(i) be probabilistic - that is, with random sampling, you could compute a mean that's no more than 20% off 99% of the time (or some other percentage) -- but not 100% unl | How do I estimate the average size of objects
An interval for the mean will
(i) be probabilistic - that is, with random sampling, you could compute a mean that's no more than 20% off 99% of the time (or some other percentage) -- but not 100% unless your sample is your population.
(ii) depend on the standard deviation ... | How do I estimate the average size of objects
An interval for the mean will
(i) be probabilistic - that is, with random sampling, you could compute a mean that's no more than 20% off 99% of the time (or some other percentage) -- but not 100% unl |
43,518 | How do I estimate the average size of objects | I would explore graphically the population by construncting some random samples of documents from your repository, then plot histograms of the number of pages for each document in the sample.
If the distributions do not resembles like a normal distribution, the mean alone is not so informative (as said in comments) an... | How do I estimate the average size of objects | I would explore graphically the population by construncting some random samples of documents from your repository, then plot histograms of the number of pages for each document in the sample.
If the | How do I estimate the average size of objects
I would explore graphically the population by construncting some random samples of documents from your repository, then plot histograms of the number of pages for each document in the sample.
If the distributions do not resembles like a normal distribution, the mean alone ... | How do I estimate the average size of objects
I would explore graphically the population by construncting some random samples of documents from your repository, then plot histograms of the number of pages for each document in the sample.
If the |
43,519 | Post-stratification & quantitative variables | Here is a short answer that I am happy for someone to edit and expand on (might do it myself if I get time).
Your first idea represents the most common approach, and would get you results that are fine. A variant on the first idea, if not all 16 groups of the 2x2 combination are well represented in the sample, is to u... | Post-stratification & quantitative variables | Here is a short answer that I am happy for someone to edit and expand on (might do it myself if I get time).
Your first idea represents the most common approach, and would get you results that are fin | Post-stratification & quantitative variables
Here is a short answer that I am happy for someone to edit and expand on (might do it myself if I get time).
Your first idea represents the most common approach, and would get you results that are fine. A variant on the first idea, if not all 16 groups of the 2x2 combinatio... | Post-stratification & quantitative variables
Here is a short answer that I am happy for someone to edit and expand on (might do it myself if I get time).
Your first idea represents the most common approach, and would get you results that are fin |
43,520 | Logistic regression and maximum entropy | TLDR; the logistic function can originate from an exponential function for different outcomes
$$p(y;x) \propto \text{exp}(f(y,x))$$
and with the normalisation using the sum $\sum_{\forall y} p(y;x)$ it becomes a logistic function
$$p(y;x) = \frac{\text{exp}(f(y,x))}{\text{other stuff}+\text{exp}(f(y,x))}$$
The connecti... | Logistic regression and maximum entropy | TLDR; the logistic function can originate from an exponential function for different outcomes
$$p(y;x) \propto \text{exp}(f(y,x))$$
and with the normalisation using the sum $\sum_{\forall y} p(y;x)$ i | Logistic regression and maximum entropy
TLDR; the logistic function can originate from an exponential function for different outcomes
$$p(y;x) \propto \text{exp}(f(y,x))$$
and with the normalisation using the sum $\sum_{\forall y} p(y;x)$ it becomes a logistic function
$$p(y;x) = \frac{\text{exp}(f(y,x))}{\text{other s... | Logistic regression and maximum entropy
TLDR; the logistic function can originate from an exponential function for different outcomes
$$p(y;x) \propto \text{exp}(f(y,x))$$
and with the normalisation using the sum $\sum_{\forall y} p(y;x)$ i |
43,521 | How can I test the difference of two Weibull distributions? | If I understand you correctly, the fact that both datasets are Weibull is really irrelevant. You are more interested in testing the two sets of results and some measure of confidence if they are from the same distribution.
In this case, the simplest approach would probably be to use a distribution-free test, such as a ... | How can I test the difference of two Weibull distributions? | If I understand you correctly, the fact that both datasets are Weibull is really irrelevant. You are more interested in testing the two sets of results and some measure of confidence if they are from | How can I test the difference of two Weibull distributions?
If I understand you correctly, the fact that both datasets are Weibull is really irrelevant. You are more interested in testing the two sets of results and some measure of confidence if they are from the same distribution.
In this case, the simplest approach w... | How can I test the difference of two Weibull distributions?
If I understand you correctly, the fact that both datasets are Weibull is really irrelevant. You are more interested in testing the two sets of results and some measure of confidence if they are from |
43,522 | How can I test the difference of two Weibull distributions? | I would first suggest that you think about what you really need. If your intent is to address failure rates, than construct a sample estimate of the observed Weibull based failure rates over time. A regression of the observed natural log of the failure rates (also called hazard rates) should be linear versus the log of... | How can I test the difference of two Weibull distributions? | I would first suggest that you think about what you really need. If your intent is to address failure rates, than construct a sample estimate of the observed Weibull based failure rates over time. A r | How can I test the difference of two Weibull distributions?
I would first suggest that you think about what you really need. If your intent is to address failure rates, than construct a sample estimate of the observed Weibull based failure rates over time. A regression of the observed natural log of the failure rates (... | How can I test the difference of two Weibull distributions?
I would first suggest that you think about what you really need. If your intent is to address failure rates, than construct a sample estimate of the observed Weibull based failure rates over time. A r |
43,523 | Test incorrect functional form when residuals have non-normal distribution | The Ramsey Test relies on the assumption of normally distributed residuals to justify the use of the F-test for exact finite sample inference for testing nested models (the linear model versus the saturated polynomial model). When the normality assumption is violated, few if any other distributions yield cogent, testab... | Test incorrect functional form when residuals have non-normal distribution | The Ramsey Test relies on the assumption of normally distributed residuals to justify the use of the F-test for exact finite sample inference for testing nested models (the linear model versus the sat | Test incorrect functional form when residuals have non-normal distribution
The Ramsey Test relies on the assumption of normally distributed residuals to justify the use of the F-test for exact finite sample inference for testing nested models (the linear model versus the saturated polynomial model). When the normality ... | Test incorrect functional form when residuals have non-normal distribution
The Ramsey Test relies on the assumption of normally distributed residuals to justify the use of the F-test for exact finite sample inference for testing nested models (the linear model versus the sat |
43,524 | Selecting optimal set of eigenvectors for Principal Components Regression | This is not possible in general. If $\boldsymbol x_i \in R^N$ is a multivariate input and $\boldsymbol y_i$ is a corresponding output. There is no a priori reason why the optimal linear relationship between the $y$s and the $\boldsymbol x$s should be a function of of the first $k$ PCs.
A counter example would be suppo... | Selecting optimal set of eigenvectors for Principal Components Regression | This is not possible in general. If $\boldsymbol x_i \in R^N$ is a multivariate input and $\boldsymbol y_i$ is a corresponding output. There is no a priori reason why the optimal linear relationship b | Selecting optimal set of eigenvectors for Principal Components Regression
This is not possible in general. If $\boldsymbol x_i \in R^N$ is a multivariate input and $\boldsymbol y_i$ is a corresponding output. There is no a priori reason why the optimal linear relationship between the $y$s and the $\boldsymbol x$s shou... | Selecting optimal set of eigenvectors for Principal Components Regression
This is not possible in general. If $\boldsymbol x_i \in R^N$ is a multivariate input and $\boldsymbol y_i$ is a corresponding output. There is no a priori reason why the optimal linear relationship b |
43,525 | Post-hoc test in two-way ANOVA | I'm not sure whether there's a better way but perhaps you can run one-way anovas on factor 1 for each category group of factor 2 separately?
You would use split files ... for this.
Try it and see whether that duplicates Andy's results. | Post-hoc test in two-way ANOVA | I'm not sure whether there's a better way but perhaps you can run one-way anovas on factor 1 for each category group of factor 2 separately?
You would use split files ... for this.
Try it and see whe | Post-hoc test in two-way ANOVA
I'm not sure whether there's a better way but perhaps you can run one-way anovas on factor 1 for each category group of factor 2 separately?
You would use split files ... for this.
Try it and see whether that duplicates Andy's results. | Post-hoc test in two-way ANOVA
I'm not sure whether there's a better way but perhaps you can run one-way anovas on factor 1 for each category group of factor 2 separately?
You would use split files ... for this.
Try it and see whe |
43,526 | Hausman-Newey test for serial correlation in Poisson with Fixed Effects | I still don't see the simple regression to get this LM test, but I just started coding and got through it. It looks like, yes, the $U_i(\hat{\beta})$ are the score function for each firm $i$ from the multinomial likelihood in 2.5. If you assume they're column vectors, then the math goes through. My results make sense a... | Hausman-Newey test for serial correlation in Poisson with Fixed Effects | I still don't see the simple regression to get this LM test, but I just started coding and got through it. It looks like, yes, the $U_i(\hat{\beta})$ are the score function for each firm $i$ from the | Hausman-Newey test for serial correlation in Poisson with Fixed Effects
I still don't see the simple regression to get this LM test, but I just started coding and got through it. It looks like, yes, the $U_i(\hat{\beta})$ are the score function for each firm $i$ from the multinomial likelihood in 2.5. If you assume the... | Hausman-Newey test for serial correlation in Poisson with Fixed Effects
I still don't see the simple regression to get this LM test, but I just started coding and got through it. It looks like, yes, the $U_i(\hat{\beta})$ are the score function for each firm $i$ from the |
43,527 | Assessing associations between many categorical variable pairs | If you really want to test pairwise associations than you should perform any test in pairwise manner. In such situation there are no problem with chi-squared test. However, I suppose you want to score and compare sizes of association but not their significances. So there are several different measures for that purpose.... | Assessing associations between many categorical variable pairs | If you really want to test pairwise associations than you should perform any test in pairwise manner. In such situation there are no problem with chi-squared test. However, I suppose you want to score | Assessing associations between many categorical variable pairs
If you really want to test pairwise associations than you should perform any test in pairwise manner. In such situation there are no problem with chi-squared test. However, I suppose you want to score and compare sizes of association but not their significa... | Assessing associations between many categorical variable pairs
If you really want to test pairwise associations than you should perform any test in pairwise manner. In such situation there are no problem with chi-squared test. However, I suppose you want to score |
43,528 | Simulating Monte Carlo with different standard deviations and interval confidence | Finish 10.000 MC runs and then start computing your confidence intervals. Compute e.g. the median value, which divides your probability distribution (PD) into two parts, where each part corresponds to 50% probability or area of your PD. Integrate your PD from -infinity to the z value covering 2.5% area of your PD, and ... | Simulating Monte Carlo with different standard deviations and interval confidence | Finish 10.000 MC runs and then start computing your confidence intervals. Compute e.g. the median value, which divides your probability distribution (PD) into two parts, where each part corresponds to | Simulating Monte Carlo with different standard deviations and interval confidence
Finish 10.000 MC runs and then start computing your confidence intervals. Compute e.g. the median value, which divides your probability distribution (PD) into two parts, where each part corresponds to 50% probability or area of your PD. I... | Simulating Monte Carlo with different standard deviations and interval confidence
Finish 10.000 MC runs and then start computing your confidence intervals. Compute e.g. the median value, which divides your probability distribution (PD) into two parts, where each part corresponds to |
43,529 | Weights argument in glmer() when predicting proportion data: why is it needed when all weights are around the same? | Even though this seems to be an old topic, and the main discussion was whether it is code or computing question, I think I could try to answer this question in some way, at least. But also with some uncertainty, though (maybe someone else could jump in with more detailed or mathematically founded answer).
Disclaimer: I... | Weights argument in glmer() when predicting proportion data: why is it needed when all weights are a | Even though this seems to be an old topic, and the main discussion was whether it is code or computing question, I think I could try to answer this question in some way, at least. But also with some u | Weights argument in glmer() when predicting proportion data: why is it needed when all weights are around the same?
Even though this seems to be an old topic, and the main discussion was whether it is code or computing question, I think I could try to answer this question in some way, at least. But also with some uncer... | Weights argument in glmer() when predicting proportion data: why is it needed when all weights are a
Even though this seems to be an old topic, and the main discussion was whether it is code or computing question, I think I could try to answer this question in some way, at least. But also with some u |
43,530 | How do I calculate random baseline? | The formula that you refer to can be used when the distribution of classes is the same in the training and test set (which is commonly assumed with machine learning).
Take 7 classes: A, B, C, D, E, F, G. There will be #A instances with label A in your data set. And of course, #A + #B + #C + #D + #E + #F + #G = X
The ch... | How do I calculate random baseline? | The formula that you refer to can be used when the distribution of classes is the same in the training and test set (which is commonly assumed with machine learning).
Take 7 classes: A, B, C, D, E, F, | How do I calculate random baseline?
The formula that you refer to can be used when the distribution of classes is the same in the training and test set (which is commonly assumed with machine learning).
Take 7 classes: A, B, C, D, E, F, G. There will be #A instances with label A in your data set. And of course, #A + #B... | How do I calculate random baseline?
The formula that you refer to can be used when the distribution of classes is the same in the training and test set (which is commonly assumed with machine learning).
Take 7 classes: A, B, C, D, E, F, |
43,531 | How to visualize a low-rank matrix decomposition? | The most effective tactic I've seen here is to compute a tSNE embedding where each observation is a row of U, then plot columns of U individually as color intensity on the tSNE embedding. | How to visualize a low-rank matrix decomposition? | The most effective tactic I've seen here is to compute a tSNE embedding where each observation is a row of U, then plot columns of U individually as color intensity on the tSNE embedding. | How to visualize a low-rank matrix decomposition?
The most effective tactic I've seen here is to compute a tSNE embedding where each observation is a row of U, then plot columns of U individually as color intensity on the tSNE embedding. | How to visualize a low-rank matrix decomposition?
The most effective tactic I've seen here is to compute a tSNE embedding where each observation is a row of U, then plot columns of U individually as color intensity on the tSNE embedding. |
43,532 | Discrepancy between ANOVA analyses | What you're trying to do here is estimate the difference between women & men in the difference between the job-applicant & no-instructions conditions in emotional intelligence score. Differences of differences are interaction effects, & must be analysed as such—it's the whole point of setting up a controlled experiment... | Discrepancy between ANOVA analyses | What you're trying to do here is estimate the difference between women & men in the difference between the job-applicant & no-instructions conditions in emotional intelligence score. Differences of di | Discrepancy between ANOVA analyses
What you're trying to do here is estimate the difference between women & men in the difference between the job-applicant & no-instructions conditions in emotional intelligence score. Differences of differences are interaction effects, & must be analysed as such—it's the whole point of... | Discrepancy between ANOVA analyses
What you're trying to do here is estimate the difference between women & men in the difference between the job-applicant & no-instructions conditions in emotional intelligence score. Differences of di |
43,533 | How to think of features in NLP problems | Indeed to have an efficient NER you need a lot of features. If you start from scratch (what I did first as well) it's really hard to figure out what features could be used other than obvious ones you mentionned. But what really boosted my scores on the one I built was introducing context grammar, tagging and parsing th... | How to think of features in NLP problems | Indeed to have an efficient NER you need a lot of features. If you start from scratch (what I did first as well) it's really hard to figure out what features could be used other than obvious ones you | How to think of features in NLP problems
Indeed to have an efficient NER you need a lot of features. If you start from scratch (what I did first as well) it's really hard to figure out what features could be used other than obvious ones you mentionned. But what really boosted my scores on the one I built was introducin... | How to think of features in NLP problems
Indeed to have an efficient NER you need a lot of features. If you start from scratch (what I did first as well) it's really hard to figure out what features could be used other than obvious ones you |
43,534 | Does trigram guarantee to perform more accurately than bigram? | As whuber explained in his comment, it depends on many factors, the most important one I believe being the information that the train set contains. E.g. if the train set is small, you're likely to have unseen trigrams, which will cause issues when tagging the test set. The choice of the size of n-gram can be seen as a ... | Does trigram guarantee to perform more accurately than bigram? | As whuber explained in his comment, it depends on many factors, the most important one I believe being the information that the train set contains. E.g. if the train set is small, you're likely to hav | Does trigram guarantee to perform more accurately than bigram?
As whuber explained in his comment, it depends on many factors, the most important one I believe being the information that the train set contains. E.g. if the train set is small, you're likely to have unseen trigrams, which will cause issues when tagging t... | Does trigram guarantee to perform more accurately than bigram?
As whuber explained in his comment, it depends on many factors, the most important one I believe being the information that the train set contains. E.g. if the train set is small, you're likely to hav |
43,535 | Does trigram guarantee to perform more accurately than bigram? | In bigram we consider past one words and in trigram we consider past two words. It can be happened that past two words itself happen less time and when it happens it contains all those probable words in same, more or less frequency. In my train set, trigram probability were same for two words where bigram probability w... | Does trigram guarantee to perform more accurately than bigram? | In bigram we consider past one words and in trigram we consider past two words. It can be happened that past two words itself happen less time and when it happens it contains all those probable words | Does trigram guarantee to perform more accurately than bigram?
In bigram we consider past one words and in trigram we consider past two words. It can be happened that past two words itself happen less time and when it happens it contains all those probable words in same, more or less frequency. In my train set, trigram... | Does trigram guarantee to perform more accurately than bigram?
In bigram we consider past one words and in trigram we consider past two words. It can be happened that past two words itself happen less time and when it happens it contains all those probable words |
43,536 | How are optional stopping rules based on e.g. sample confidence (width of confidence interval) biased? | Jan Vanhove presented simulations showing that optional stopping based on the width of a confidence interval does not introduce biases. He simulated a situation where the null hypothesis was true, and simulated thousands of experiments that continued adding n until the confidence interval was narrower than a prespecifi... | How are optional stopping rules based on e.g. sample confidence (width of confidence interval) biase | Jan Vanhove presented simulations showing that optional stopping based on the width of a confidence interval does not introduce biases. He simulated a situation where the null hypothesis was true, and | How are optional stopping rules based on e.g. sample confidence (width of confidence interval) biased?
Jan Vanhove presented simulations showing that optional stopping based on the width of a confidence interval does not introduce biases. He simulated a situation where the null hypothesis was true, and simulated thousa... | How are optional stopping rules based on e.g. sample confidence (width of confidence interval) biase
Jan Vanhove presented simulations showing that optional stopping based on the width of a confidence interval does not introduce biases. He simulated a situation where the null hypothesis was true, and |
43,537 | Comparing regression coefficients across models with standardized dependent variables | No, you cannot state that an independent variable has twice as large an impact on one DV (dependent variable) as another DV merely by comparing coefficients in the models. Why? Because your dependent variables are not measuring comparable quantities in all four cases above.
Let's take a different example to highlight ... | Comparing regression coefficients across models with standardized dependent variables | No, you cannot state that an independent variable has twice as large an impact on one DV (dependent variable) as another DV merely by comparing coefficients in the models. Why? Because your dependent | Comparing regression coefficients across models with standardized dependent variables
No, you cannot state that an independent variable has twice as large an impact on one DV (dependent variable) as another DV merely by comparing coefficients in the models. Why? Because your dependent variables are not measuring compar... | Comparing regression coefficients across models with standardized dependent variables
No, you cannot state that an independent variable has twice as large an impact on one DV (dependent variable) as another DV merely by comparing coefficients in the models. Why? Because your dependent |
43,538 | When would maximum likelihood estimates equal least squares estimates? | When the statistical properties of the underlying data-generating process are "normal", i.e., error terms are Gaussian distributed and iid. In this case, the maximum likelihood estimator is equivalent to the least-squares estimator. | When would maximum likelihood estimates equal least squares estimates? | When the statistical properties of the underlying data-generating process are "normal", i.e., error terms are Gaussian distributed and iid. In this case, the maximum likelihood estimator is equivalent | When would maximum likelihood estimates equal least squares estimates?
When the statistical properties of the underlying data-generating process are "normal", i.e., error terms are Gaussian distributed and iid. In this case, the maximum likelihood estimator is equivalent to the least-squares estimator. | When would maximum likelihood estimates equal least squares estimates?
When the statistical properties of the underlying data-generating process are "normal", i.e., error terms are Gaussian distributed and iid. In this case, the maximum likelihood estimator is equivalent |
43,539 | When would maximum likelihood estimates equal least squares estimates? | In MLE there is no L2 norm. Imagine doing logistic regression (LR). This is an MLE problem. The objective function for this, and for any MLE problem, is the likelihood or the probability of Y, given X (N x p) and beta. The responses for LR are N-dimensional sequences of {H, T} or {T, F}. There is no natural way to... | When would maximum likelihood estimates equal least squares estimates? | In MLE there is no L2 norm. Imagine doing logistic regression (LR). This is an MLE problem. The objective function for this, and for any MLE problem, is the likelihood or the probability of Y, give | When would maximum likelihood estimates equal least squares estimates?
In MLE there is no L2 norm. Imagine doing logistic regression (LR). This is an MLE problem. The objective function for this, and for any MLE problem, is the likelihood or the probability of Y, given X (N x p) and beta. The responses for LR are N... | When would maximum likelihood estimates equal least squares estimates?
In MLE there is no L2 norm. Imagine doing logistic regression (LR). This is an MLE problem. The objective function for this, and for any MLE problem, is the likelihood or the probability of Y, give |
43,540 | Non-linear modelling with several variables including a categorical variable [duplicate] | I apologize for the incredibly late response, but I came across the same problem recently. I found that it is possible to code categorical variables with nls(), simply by multiplying true/false vectors into your equation. Example:
# null model (no difference between groups; all have the same coefficients)
nls.null <- n... | Non-linear modelling with several variables including a categorical variable [duplicate] | I apologize for the incredibly late response, but I came across the same problem recently. I found that it is possible to code categorical variables with nls(), simply by multiplying true/false vector | Non-linear modelling with several variables including a categorical variable [duplicate]
I apologize for the incredibly late response, but I came across the same problem recently. I found that it is possible to code categorical variables with nls(), simply by multiplying true/false vectors into your equation. Example:
... | Non-linear modelling with several variables including a categorical variable [duplicate]
I apologize for the incredibly late response, but I came across the same problem recently. I found that it is possible to code categorical variables with nls(), simply by multiplying true/false vector |
43,541 | How to encode an n-level categorical variable as dummies, for glmnet? | "In the extreme case of k identical predictors, they each get identical coefficients with 1=kth the size that any single one would get if t alone. From a Bayesian point of view, the ridge penalty is ideal if there are many predictors, and all have non-zero coefficients (drawn from a Gaussian distribution).
Lasso, on th... | How to encode an n-level categorical variable as dummies, for glmnet? | "In the extreme case of k identical predictors, they each get identical coefficients with 1=kth the size that any single one would get if t alone. From a Bayesian point of view, the ridge penalty is i | How to encode an n-level categorical variable as dummies, for glmnet?
"In the extreme case of k identical predictors, they each get identical coefficients with 1=kth the size that any single one would get if t alone. From a Bayesian point of view, the ridge penalty is ideal if there are many predictors, and all have no... | How to encode an n-level categorical variable as dummies, for glmnet?
"In the extreme case of k identical predictors, they each get identical coefficients with 1=kth the size that any single one would get if t alone. From a Bayesian point of view, the ridge penalty is i |
43,542 | How to encode an n-level categorical variable as dummies, for glmnet? | The question seems have little thing to do with glmnet, but factor encoding and interpretation on coefficient in general.
I would suggest to look at logistic regression that uses categorical variables as a start. R Library: Contrast Coding Systems for categorical variables a great resource to learn different types of ... | How to encode an n-level categorical variable as dummies, for glmnet? | The question seems have little thing to do with glmnet, but factor encoding and interpretation on coefficient in general.
I would suggest to look at logistic regression that uses categorical variable | How to encode an n-level categorical variable as dummies, for glmnet?
The question seems have little thing to do with glmnet, but factor encoding and interpretation on coefficient in general.
I would suggest to look at logistic regression that uses categorical variables as a start. R Library: Contrast Coding Systems f... | How to encode an n-level categorical variable as dummies, for glmnet?
The question seems have little thing to do with glmnet, but factor encoding and interpretation on coefficient in general.
I would suggest to look at logistic regression that uses categorical variable |
43,543 | Population model to model year to year dynamics | Baker 2012 (Journal of Applied Ecology) used similar model. I was asking him and he replied he uses normal glm()! He inspired me to use the following transformation (that he actually used in the linked article) - just recursively substitute the $\mbox{log} (\mu_{i,j})$, until you get this:
$$\mbox{log} ( \mu_{i,j+1} ) ... | Population model to model year to year dynamics | Baker 2012 (Journal of Applied Ecology) used similar model. I was asking him and he replied he uses normal glm()! He inspired me to use the following transformation (that he actually used in the linke | Population model to model year to year dynamics
Baker 2012 (Journal of Applied Ecology) used similar model. I was asking him and he replied he uses normal glm()! He inspired me to use the following transformation (that he actually used in the linked article) - just recursively substitute the $\mbox{log} (\mu_{i,j})$, u... | Population model to model year to year dynamics
Baker 2012 (Journal of Applied Ecology) used similar model. I was asking him and he replied he uses normal glm()! He inspired me to use the following transformation (that he actually used in the linke |
43,544 | Population model to model year to year dynamics | Population growth models often use Poisson modeling framework. In R, fitting a Poisson GLM is easy. See ?glm. An example is:
f <- glm(N ~ x + R, family=poisson)
To estimate: $\log(\mu | x) = \beta_0 + \beta_1 x + \beta_2 R$. $\beta_1$ is interpreted as a relative rate comparing the rate (or Poisson lambda) for $N$ diff... | Population model to model year to year dynamics | Population growth models often use Poisson modeling framework. In R, fitting a Poisson GLM is easy. See ?glm. An example is:
f <- glm(N ~ x + R, family=poisson)
To estimate: $\log(\mu | x) = \beta_0 + | Population model to model year to year dynamics
Population growth models often use Poisson modeling framework. In R, fitting a Poisson GLM is easy. See ?glm. An example is:
f <- glm(N ~ x + R, family=poisson)
To estimate: $\log(\mu | x) = \beta_0 + \beta_1 x + \beta_2 R$. $\beta_1$ is interpreted as a relative rate com... | Population model to model year to year dynamics
Population growth models often use Poisson modeling framework. In R, fitting a Poisson GLM is easy. See ?glm. An example is:
f <- glm(N ~ x + R, family=poisson)
To estimate: $\log(\mu | x) = \beta_0 + |
43,545 | Which statistical test to use with multiple response variables and continuous predictors? | I would suggest you to clearly mention you the number and measurement scale (metric or non-metric) response and predictor variables in order to make others understand the problem correctly.
If I understood correctly, you have multivariate multiple regression problem. One possible solution is to use MANCOVA (multivariat... | Which statistical test to use with multiple response variables and continuous predictors? | I would suggest you to clearly mention you the number and measurement scale (metric or non-metric) response and predictor variables in order to make others understand the problem correctly.
If I under | Which statistical test to use with multiple response variables and continuous predictors?
I would suggest you to clearly mention you the number and measurement scale (metric or non-metric) response and predictor variables in order to make others understand the problem correctly.
If I understood correctly, you have mult... | Which statistical test to use with multiple response variables and continuous predictors?
I would suggest you to clearly mention you the number and measurement scale (metric or non-metric) response and predictor variables in order to make others understand the problem correctly.
If I under |
43,546 | Importance sampling of finite path of stochastic difference equation | This isn't a direct answer to your question, but what you have described sounds very much like a hidden Markov model and particle filtering. I'm not sure if you are familiar with filtering theory, but here is a particle filtering tutorial that I have found helpful in the past. If you don't have access to that paper, th... | Importance sampling of finite path of stochastic difference equation | This isn't a direct answer to your question, but what you have described sounds very much like a hidden Markov model and particle filtering. I'm not sure if you are familiar with filtering theory, but | Importance sampling of finite path of stochastic difference equation
This isn't a direct answer to your question, but what you have described sounds very much like a hidden Markov model and particle filtering. I'm not sure if you are familiar with filtering theory, but here is a particle filtering tutorial that I have ... | Importance sampling of finite path of stochastic difference equation
This isn't a direct answer to your question, but what you have described sounds very much like a hidden Markov model and particle filtering. I'm not sure if you are familiar with filtering theory, but |
43,547 | Starting coefficient vector for GLM | R's glm does not (by default) start with an initial value for $\beta$, it starts with an initial value for $\mu$. The initial value for $\mu$ depends on the family; it is close to $y$ but chosen to be in the domain of the likely link function. For example, for binomial, with $y=r/n$,
$$\mu=\frac{r+1/2}{n-r+1/2}$$
and... | Starting coefficient vector for GLM | R's glm does not (by default) start with an initial value for $\beta$, it starts with an initial value for $\mu$. The initial value for $\mu$ depends on the family; it is close to $y$ but chosen to b | Starting coefficient vector for GLM
R's glm does not (by default) start with an initial value for $\beta$, it starts with an initial value for $\mu$. The initial value for $\mu$ depends on the family; it is close to $y$ but chosen to be in the domain of the likely link function. For example, for binomial, with $y=r/n... | Starting coefficient vector for GLM
R's glm does not (by default) start with an initial value for $\beta$, it starts with an initial value for $\mu$. The initial value for $\mu$ depends on the family; it is close to $y$ but chosen to b |
43,548 | Starting coefficient vector for GLM | Well, after much searching and going through papers on the theory behind GLM, I found this algorithm for the initial values, which numerically agrees with R using maxit = 1 to force R to output its initial coefficient estimates. | Starting coefficient vector for GLM | Well, after much searching and going through papers on the theory behind GLM, I found this algorithm for the initial values, which numerically agrees with R using maxit = 1 to force R to output its in | Starting coefficient vector for GLM
Well, after much searching and going through papers on the theory behind GLM, I found this algorithm for the initial values, which numerically agrees with R using maxit = 1 to force R to output its initial coefficient estimates. | Starting coefficient vector for GLM
Well, after much searching and going through papers on the theory behind GLM, I found this algorithm for the initial values, which numerically agrees with R using maxit = 1 to force R to output its in |
43,549 | Randomly distributed residuals or not? | I see you use a categorical variable as a continuous. You should distinguish each case with three dummy variables. A longer explanation is provided here.
That being said, I would re-estimate your model and check the residuals once more, with a better specified model.
The DW statistics is used for time-series. Is it the... | Randomly distributed residuals or not? | I see you use a categorical variable as a continuous. You should distinguish each case with three dummy variables. A longer explanation is provided here.
That being said, I would re-estimate your mode | Randomly distributed residuals or not?
I see you use a categorical variable as a continuous. You should distinguish each case with three dummy variables. A longer explanation is provided here.
That being said, I would re-estimate your model and check the residuals once more, with a better specified model.
The DW statis... | Randomly distributed residuals or not?
I see you use a categorical variable as a continuous. You should distinguish each case with three dummy variables. A longer explanation is provided here.
That being said, I would re-estimate your mode |
43,550 | What do you consider a new model versus an updated model (time series)? | The question you raise is quite important. WE have implemented the CHOW Test for constancy of parameters in order to test the hypothesis that the parameters haven't changed significantly at one or more points in time. If we detect a significant change then we can then use the most recent data set to develop a new model... | What do you consider a new model versus an updated model (time series)? | The question you raise is quite important. WE have implemented the CHOW Test for constancy of parameters in order to test the hypothesis that the parameters haven't changed significantly at one or mor | What do you consider a new model versus an updated model (time series)?
The question you raise is quite important. WE have implemented the CHOW Test for constancy of parameters in order to test the hypothesis that the parameters haven't changed significantly at one or more points in time. If we detect a significant cha... | What do you consider a new model versus an updated model (time series)?
The question you raise is quite important. WE have implemented the CHOW Test for constancy of parameters in order to test the hypothesis that the parameters haven't changed significantly at one or mor |
43,551 | What exactly is the equation for SVM classification for new example? | Burges's A Tutorial on Support Vector Machines for Pattern Recognition provides you a very detailed introduction to SVMs. Just compare it to logistic regression, the decision function of SVMs for the binary classification case is
$$
f(\mathbf{x}) = \text{sgn} (\mathbf{w}^T \mathbf{x} + b)
$$
where $\mathbf{w} = \sum_i ... | What exactly is the equation for SVM classification for new example? | Burges's A Tutorial on Support Vector Machines for Pattern Recognition provides you a very detailed introduction to SVMs. Just compare it to logistic regression, the decision function of SVMs for the | What exactly is the equation for SVM classification for new example?
Burges's A Tutorial on Support Vector Machines for Pattern Recognition provides you a very detailed introduction to SVMs. Just compare it to logistic regression, the decision function of SVMs for the binary classification case is
$$
f(\mathbf{x}) = \t... | What exactly is the equation for SVM classification for new example?
Burges's A Tutorial on Support Vector Machines for Pattern Recognition provides you a very detailed introduction to SVMs. Just compare it to logistic regression, the decision function of SVMs for the |
43,552 | Choosing one variable from each of 3 buckets of variables | Since you have three categorical variables with $20$ categories each, plus gender and age, that gives you a total of $3 \times 19+1 = 58$ binary variables and one continuous variable. If you are willing to proceed without interaction effects, that gives you a model with $60$ coefficients (including an intercept term).... | Choosing one variable from each of 3 buckets of variables | Since you have three categorical variables with $20$ categories each, plus gender and age, that gives you a total of $3 \times 19+1 = 58$ binary variables and one continuous variable. If you are will | Choosing one variable from each of 3 buckets of variables
Since you have three categorical variables with $20$ categories each, plus gender and age, that gives you a total of $3 \times 19+1 = 58$ binary variables and one continuous variable. If you are willing to proceed without interaction effects, that gives you a m... | Choosing one variable from each of 3 buckets of variables
Since you have three categorical variables with $20$ categories each, plus gender and age, that gives you a total of $3 \times 19+1 = 58$ binary variables and one continuous variable. If you are will |
43,553 | Choosing one variable from each of 3 buckets of variables | If you have 60 possible covariates, and just want to be able to use the model to build predictions and are not that concerned with interpretability, you might build a random forest on a training set of your data and see what kind of predictive power you could get from the model it builds. The package randomForest in r... | Choosing one variable from each of 3 buckets of variables | If you have 60 possible covariates, and just want to be able to use the model to build predictions and are not that concerned with interpretability, you might build a random forest on a training set o | Choosing one variable from each of 3 buckets of variables
If you have 60 possible covariates, and just want to be able to use the model to build predictions and are not that concerned with interpretability, you might build a random forest on a training set of your data and see what kind of predictive power you could ge... | Choosing one variable from each of 3 buckets of variables
If you have 60 possible covariates, and just want to be able to use the model to build predictions and are not that concerned with interpretability, you might build a random forest on a training set o |
43,554 | Dimensionality Reduction Algorithm for Large Dataset? | Random forests are robust. They are not impacted by outliers.
Gradient boosted trees are great at fitting or over fitting the data.
The combination is fast, handles classical or categorical data, and can handle very large data.
Random forests of gradient boosted trees easily handle problems of this complexity and size... | Dimensionality Reduction Algorithm for Large Dataset? | Random forests are robust. They are not impacted by outliers.
Gradient boosted trees are great at fitting or over fitting the data.
The combination is fast, handles classical or categorical data, and | Dimensionality Reduction Algorithm for Large Dataset?
Random forests are robust. They are not impacted by outliers.
Gradient boosted trees are great at fitting or over fitting the data.
The combination is fast, handles classical or categorical data, and can handle very large data.
Random forests of gradient boosted tr... | Dimensionality Reduction Algorithm for Large Dataset?
Random forests are robust. They are not impacted by outliers.
Gradient boosted trees are great at fitting or over fitting the data.
The combination is fast, handles classical or categorical data, and |
43,555 | Are GAMMs/GLM the best choice for calculating number of germs on hands? | I think the model is incomplete. Why not consider her hands as a forest of SIR models? There are some things that "die on the vine" and for that "R" applies.
Here are links on SIR:
http://www.maa.org/publications/periodicals/loci/joma/the-sir-model-for-spread-of-disease-the-differential-equation-model
http://www.i... | Are GAMMs/GLM the best choice for calculating number of germs on hands? | I think the model is incomplete. Why not consider her hands as a forest of SIR models? There are some things that "die on the vine" and for that "R" applies.
Here are links on SIR:
http://www.maa | Are GAMMs/GLM the best choice for calculating number of germs on hands?
I think the model is incomplete. Why not consider her hands as a forest of SIR models? There are some things that "die on the vine" and for that "R" applies.
Here are links on SIR:
http://www.maa.org/publications/periodicals/loci/joma/the-sir-... | Are GAMMs/GLM the best choice for calculating number of germs on hands?
I think the model is incomplete. Why not consider her hands as a forest of SIR models? There are some things that "die on the vine" and for that "R" applies.
Here are links on SIR:
http://www.maa |
43,556 | Checking for outliers in a glmer (lme4 package) with 3 random factors | try the romr.fnc in the LMERConvenienceFunctions to remove outliers
df3.trimmed = romr.fnc(m, df3, trim = 2.5)
df3.trimmed = df3.trimmed$data
update initial model on trimmed data
mB = update(m1) | Checking for outliers in a glmer (lme4 package) with 3 random factors | try the romr.fnc in the LMERConvenienceFunctions to remove outliers
df3.trimmed = romr.fnc(m, df3, trim = 2.5)
df3.trimmed = df3.trimmed$data
update initial model on trimmed data
mB = update(m1) | Checking for outliers in a glmer (lme4 package) with 3 random factors
try the romr.fnc in the LMERConvenienceFunctions to remove outliers
df3.trimmed = romr.fnc(m, df3, trim = 2.5)
df3.trimmed = df3.trimmed$data
update initial model on trimmed data
mB = update(m1) | Checking for outliers in a glmer (lme4 package) with 3 random factors
try the romr.fnc in the LMERConvenienceFunctions to remove outliers
df3.trimmed = romr.fnc(m, df3, trim = 2.5)
df3.trimmed = df3.trimmed$data
update initial model on trimmed data
mB = update(m1) |
43,557 | What are the assumptions for checking the stationarity of a time series? | ADF is a parametric test and KPSS is a non-parametric test of unit root. That being said, the chosen lag order in the ADF should be such that residuals are white noise. | What are the assumptions for checking the stationarity of a time series? | ADF is a parametric test and KPSS is a non-parametric test of unit root. That being said, the chosen lag order in the ADF should be such that residuals are white noise. | What are the assumptions for checking the stationarity of a time series?
ADF is a parametric test and KPSS is a non-parametric test of unit root. That being said, the chosen lag order in the ADF should be such that residuals are white noise. | What are the assumptions for checking the stationarity of a time series?
ADF is a parametric test and KPSS is a non-parametric test of unit root. That being said, the chosen lag order in the ADF should be such that residuals are white noise. |
43,558 | How to model number of days in the last week smoking cigarettes (0 to 7 - 'U' shaped)? | You might want to take a look at two-part (aka hurdle) count data models. A good place to start is Chapter 17 of Cameron and Trivedi's Microeconometrics using Stata. In fact, your smoking example is the one they use to motivate this. Essentially, you have one model to determine if a person takes up smoking, and then an... | How to model number of days in the last week smoking cigarettes (0 to 7 - 'U' shaped)? | You might want to take a look at two-part (aka hurdle) count data models. A good place to start is Chapter 17 of Cameron and Trivedi's Microeconometrics using Stata. In fact, your smoking example is t | How to model number of days in the last week smoking cigarettes (0 to 7 - 'U' shaped)?
You might want to take a look at two-part (aka hurdle) count data models. A good place to start is Chapter 17 of Cameron and Trivedi's Microeconometrics using Stata. In fact, your smoking example is the one they use to motivate this.... | How to model number of days in the last week smoking cigarettes (0 to 7 - 'U' shaped)?
You might want to take a look at two-part (aka hurdle) count data models. A good place to start is Chapter 17 of Cameron and Trivedi's Microeconometrics using Stata. In fact, your smoking example is t |
43,559 | How to model number of days in the last week smoking cigarettes (0 to 7 - 'U' shaped)? | Think about the construct of interest
I'd think about the construct you are trying to measure. As Macro mentioned, it may be that your variable is largely reflecting the fact that people are either smokers or not smokers. If they are smokers, they will tend to smoke every day of the week, and if they are not smokers, t... | How to model number of days in the last week smoking cigarettes (0 to 7 - 'U' shaped)? | Think about the construct of interest
I'd think about the construct you are trying to measure. As Macro mentioned, it may be that your variable is largely reflecting the fact that people are either sm | How to model number of days in the last week smoking cigarettes (0 to 7 - 'U' shaped)?
Think about the construct of interest
I'd think about the construct you are trying to measure. As Macro mentioned, it may be that your variable is largely reflecting the fact that people are either smokers or not smokers. If they are... | How to model number of days in the last week smoking cigarettes (0 to 7 - 'U' shaped)?
Think about the construct of interest
I'd think about the construct you are trying to measure. As Macro mentioned, it may be that your variable is largely reflecting the fact that people are either sm |
43,560 | Comparing coefficients of time series models | The Chow Test can be used to test the equivalence of two (or more ) time series models. | Comparing coefficients of time series models | The Chow Test can be used to test the equivalence of two (or more ) time series models. | Comparing coefficients of time series models
The Chow Test can be used to test the equivalence of two (or more ) time series models. | Comparing coefficients of time series models
The Chow Test can be used to test the equivalence of two (or more ) time series models. |
43,561 | Program yourself or use data mining toolkit? | I think I agree, drag/drop approach for data mining is not want you need. I think you need something similar to Python-like scripting language. If you think about such approach to statistics, you can try ScaVis program that use Python for statistics. Another option is SciPy or similar. | Program yourself or use data mining toolkit? | I think I agree, drag/drop approach for data mining is not want you need. I think you need something similar to Python-like scripting language. If you think about such approach to statistics, you can | Program yourself or use data mining toolkit?
I think I agree, drag/drop approach for data mining is not want you need. I think you need something similar to Python-like scripting language. If you think about such approach to statistics, you can try ScaVis program that use Python for statistics. Another option is SciPy ... | Program yourself or use data mining toolkit?
I think I agree, drag/drop approach for data mining is not want you need. I think you need something similar to Python-like scripting language. If you think about such approach to statistics, you can |
43,562 | Logistic Regression with dependent observations | You could always come up with a set of transformed variables that aggregate the data from 3 months into one observation for each patient (e.g., average blood pressure across the prior 3 months, 3-month exercise hours/cigarette, etc.). Then you have independent observations (1 per patient), and you could build the mode... | Logistic Regression with dependent observations | You could always come up with a set of transformed variables that aggregate the data from 3 months into one observation for each patient (e.g., average blood pressure across the prior 3 months, 3-mont | Logistic Regression with dependent observations
You could always come up with a set of transformed variables that aggregate the data from 3 months into one observation for each patient (e.g., average blood pressure across the prior 3 months, 3-month exercise hours/cigarette, etc.). Then you have independent observatio... | Logistic Regression with dependent observations
You could always come up with a set of transformed variables that aggregate the data from 3 months into one observation for each patient (e.g., average blood pressure across the prior 3 months, 3-mont |
43,563 | Is it valid to take a mean of p-values during cross-validation, when comparing the predicted output of a model to the actual output? [closed] | How about using Fisher's method or Stouffer's method for combining independent p-values to reject the global null hypothesis ? In your case, I reckon the global null hypothesis is the training and test data follows the same distribution. For more information, you can visit the following pages
When combining p-values, w... | Is it valid to take a mean of p-values during cross-validation, when comparing the predicted output | How about using Fisher's method or Stouffer's method for combining independent p-values to reject the global null hypothesis ? In your case, I reckon the global null hypothesis is the training and tes | Is it valid to take a mean of p-values during cross-validation, when comparing the predicted output of a model to the actual output? [closed]
How about using Fisher's method or Stouffer's method for combining independent p-values to reject the global null hypothesis ? In your case, I reckon the global null hypothesis i... | Is it valid to take a mean of p-values during cross-validation, when comparing the predicted output
How about using Fisher's method or Stouffer's method for combining independent p-values to reject the global null hypothesis ? In your case, I reckon the global null hypothesis is the training and tes |
43,564 | How to test for outliers in an mlogit model in R | I assume that what you want is a diagnostic plot of some sort that examines residuals against fitted values. Typically model outliers are observations whose fitted values $\hat{y}$ are very different from their observed values $y$. In other words, they have an abnormally large residual $\epsilon = y - \hat{y}$.
The tri... | How to test for outliers in an mlogit model in R | I assume that what you want is a diagnostic plot of some sort that examines residuals against fitted values. Typically model outliers are observations whose fitted values $\hat{y}$ are very different | How to test for outliers in an mlogit model in R
I assume that what you want is a diagnostic plot of some sort that examines residuals against fitted values. Typically model outliers are observations whose fitted values $\hat{y}$ are very different from their observed values $y$. In other words, they have an abnormally... | How to test for outliers in an mlogit model in R
I assume that what you want is a diagnostic plot of some sort that examines residuals against fitted values. Typically model outliers are observations whose fitted values $\hat{y}$ are very different |
43,565 | Confusion related to semisupervised learning in random walk | Consider you have $n$ data points (or examples) of which $l$ are labeled and $u$ are unlabeled with $l \ll n$ and $ n = l + u $. Also, assume you have $m$ classes. Let us assign an $m$-dimensional label vector $\mathbf{y}_i \in [1, 0]^m$ with data point $i$. You can interpret the $j$th element of $\mathbf{y}_i$ as the ... | Confusion related to semisupervised learning in random walk | Consider you have $n$ data points (or examples) of which $l$ are labeled and $u$ are unlabeled with $l \ll n$ and $ n = l + u $. Also, assume you have $m$ classes. Let us assign an $m$-dimensional lab | Confusion related to semisupervised learning in random walk
Consider you have $n$ data points (or examples) of which $l$ are labeled and $u$ are unlabeled with $l \ll n$ and $ n = l + u $. Also, assume you have $m$ classes. Let us assign an $m$-dimensional label vector $\mathbf{y}_i \in [1, 0]^m$ with data point $i$. Y... | Confusion related to semisupervised learning in random walk
Consider you have $n$ data points (or examples) of which $l$ are labeled and $u$ are unlabeled with $l \ll n$ and $ n = l + u $. Also, assume you have $m$ classes. Let us assign an $m$-dimensional lab |
43,566 | Can I validly expand the odds ratio analogously to the relationship between relative risk & the incidence rate ratio? | This link provides a decent discussion of this: https://www.ctspedia.org/do/view/CTSpedia/SampleIncidence
Your answer is yes, conditionally. If you are using incidence density sampling (that is that controls are sampled from the risk set each time a case is diagnosed) which effectively matches cases and controls for ti... | Can I validly expand the odds ratio analogously to the relationship between relative risk & the inci | This link provides a decent discussion of this: https://www.ctspedia.org/do/view/CTSpedia/SampleIncidence
Your answer is yes, conditionally. If you are using incidence density sampling (that is that c | Can I validly expand the odds ratio analogously to the relationship between relative risk & the incidence rate ratio?
This link provides a decent discussion of this: https://www.ctspedia.org/do/view/CTSpedia/SampleIncidence
Your answer is yes, conditionally. If you are using incidence density sampling (that is that con... | Can I validly expand the odds ratio analogously to the relationship between relative risk & the inci
This link provides a decent discussion of this: https://www.ctspedia.org/do/view/CTSpedia/SampleIncidence
Your answer is yes, conditionally. If you are using incidence density sampling (that is that c |
43,567 | Can I validly expand the odds ratio analogously to the relationship between relative risk & the incidence rate ratio? | I'm not sure that your approach would give a valid $IRR$ approximation. First, you would be "looking into the future" to determine the person-time at risk through this approach, since you know cases/controls. Generally speaking, looking into the future like that causes all sorts of problems. Second, there is a stipulat... | Can I validly expand the odds ratio analogously to the relationship between relative risk & the inci | I'm not sure that your approach would give a valid $IRR$ approximation. First, you would be "looking into the future" to determine the person-time at risk through this approach, since you know cases/c | Can I validly expand the odds ratio analogously to the relationship between relative risk & the incidence rate ratio?
I'm not sure that your approach would give a valid $IRR$ approximation. First, you would be "looking into the future" to determine the person-time at risk through this approach, since you know cases/con... | Can I validly expand the odds ratio analogously to the relationship between relative risk & the inci
I'm not sure that your approach would give a valid $IRR$ approximation. First, you would be "looking into the future" to determine the person-time at risk through this approach, since you know cases/c |
43,568 | How do you interpret the results from ridge regression? | Some things to look at when fitting the ridge regression
regression coefficients for this fit:
round(gridge$coef[, which(gridge$lambda ==.02)], 2)
ordinary least square fit:
round(gridge$coef[, which(gridge$lambda == 0)], 2)
The ridge regression centers and scales the predictors so you need to do the same when ca... | How do you interpret the results from ridge regression? | Some things to look at when fitting the ridge regression
regression coefficients for this fit:
round(gridge$coef[, which(gridge$lambda ==.02)], 2)
ordinary least square fit:
round(gridge$coef[, w | How do you interpret the results from ridge regression?
Some things to look at when fitting the ridge regression
regression coefficients for this fit:
round(gridge$coef[, which(gridge$lambda ==.02)], 2)
ordinary least square fit:
round(gridge$coef[, which(gridge$lambda == 0)], 2)
The ridge regression centers and ... | How do you interpret the results from ridge regression?
Some things to look at when fitting the ridge regression
regression coefficients for this fit:
round(gridge$coef[, which(gridge$lambda ==.02)], 2)
ordinary least square fit:
round(gridge$coef[, w |
43,569 | Prediction interval for robust regression with MM-estimator | It would be easier to answer the question if we had the actual formula for the estimator. But generally speaking the exact distribution of the estimator should depend on the error distribution. However the covariance matrix can be estimated from the data. An exact prediction interval would seem to depend on the dist... | Prediction interval for robust regression with MM-estimator | It would be easier to answer the question if we had the actual formula for the estimator. But generally speaking the exact distribution of the estimator should depend on the error distribution. Howe | Prediction interval for robust regression with MM-estimator
It would be easier to answer the question if we had the actual formula for the estimator. But generally speaking the exact distribution of the estimator should depend on the error distribution. However the covariance matrix can be estimated from the data. A... | Prediction interval for robust regression with MM-estimator
It would be easier to answer the question if we had the actual formula for the estimator. But generally speaking the exact distribution of the estimator should depend on the error distribution. Howe |
43,570 | Convergence of MCMC for ill-behaved functions | There are different routes suggested in the literature, but none of them is foolproof to check that
one has reached stationarity in the sense that $X_t\sim\pi(x)$ marginally;
one has explored the stationary distribution in the sense that$$\frac{1}{T}\sum_{t=1}^T h(X_t)\approx\mathbb{E}^\pi[h(X)]\tag{2}$$
(which are t... | Convergence of MCMC for ill-behaved functions | There are different routes suggested in the literature, but none of them is foolproof to check that
one has reached stationarity in the sense that $X_t\sim\pi(x)$ marginally;
one has explored the sta | Convergence of MCMC for ill-behaved functions
There are different routes suggested in the literature, but none of them is foolproof to check that
one has reached stationarity in the sense that $X_t\sim\pi(x)$ marginally;
one has explored the stationary distribution in the sense that$$\frac{1}{T}\sum_{t=1}^T h(X_t)\app... | Convergence of MCMC for ill-behaved functions
There are different routes suggested in the literature, but none of them is foolproof to check that
one has reached stationarity in the sense that $X_t\sim\pi(x)$ marginally;
one has explored the sta |
43,571 | How could I extract the distribution of one RV when given a set of sums of two RVs? | If $X$ has density $f$ and $Y$ has density $g$ and $X$ and $Y$ are independent then $Z=X+Y$ has a density called the convolution of $f$ and $g$.
$$H(z)=P(Z\le z) = \int \int f(x) g(y)dy dx$$
where $x$ runs from $-\infty$ to $\infty$ and $y$ from $-\infty$ to $z-x$. Then $h(z)=H^\prime(z)$.
So the question becomes if ... | How could I extract the distribution of one RV when given a set of sums of two RVs? | If $X$ has density $f$ and $Y$ has density $g$ and $X$ and $Y$ are independent then $Z=X+Y$ has a density called the convolution of $f$ and $g$.
$$H(z)=P(Z\le z) = \int \int f(x) g(y)dy dx$$
where $ | How could I extract the distribution of one RV when given a set of sums of two RVs?
If $X$ has density $f$ and $Y$ has density $g$ and $X$ and $Y$ are independent then $Z=X+Y$ has a density called the convolution of $f$ and $g$.
$$H(z)=P(Z\le z) = \int \int f(x) g(y)dy dx$$
where $x$ runs from $-\infty$ to $\infty$ a... | How could I extract the distribution of one RV when given a set of sums of two RVs?
If $X$ has density $f$ and $Y$ has density $g$ and $X$ and $Y$ are independent then $Z=X+Y$ has a density called the convolution of $f$ and $g$.
$$H(z)=P(Z\le z) = \int \int f(x) g(y)dy dx$$
where $ |
43,572 | Distribution of a centered standardized sample | EDIT: I guess you wanted a finite-sample result, not an asymptotic one. But if $n$ is large, the argument below shows why you'll be close to independent Normals.
Isn't this just an application of Slutsky's Theorem? Write:
$$\frac{X - m}{s} = \frac{\sigma}{s}\frac{X - \mu}{\sigma} + \frac{\sigma}{s}\frac{\mu - m}{\sigma... | Distribution of a centered standardized sample | EDIT: I guess you wanted a finite-sample result, not an asymptotic one. But if $n$ is large, the argument below shows why you'll be close to independent Normals.
Isn't this just an application of Slut | Distribution of a centered standardized sample
EDIT: I guess you wanted a finite-sample result, not an asymptotic one. But if $n$ is large, the argument below shows why you'll be close to independent Normals.
Isn't this just an application of Slutsky's Theorem? Write:
$$\frac{X - m}{s} = \frac{\sigma}{s}\frac{X - \mu}{... | Distribution of a centered standardized sample
EDIT: I guess you wanted a finite-sample result, not an asymptotic one. But if $n$ is large, the argument below shows why you'll be close to independent Normals.
Isn't this just an application of Slut |
43,573 | Distribution of a centered standardized sample | Answering to myself : I no longer think that this problem is (directly) solvable, because the transformation I study (i. e. centering and standardizing) is not a bijection : $Z=\alpha{}X$ will have the same image as $X$ (whenever $\alpha\neq{}0$, of course), as well as $T=X+\gamma$. Therefore, the classic "Jacobian" re... | Distribution of a centered standardized sample | Answering to myself : I no longer think that this problem is (directly) solvable, because the transformation I study (i. e. centering and standardizing) is not a bijection : $Z=\alpha{}X$ will have th | Distribution of a centered standardized sample
Answering to myself : I no longer think that this problem is (directly) solvable, because the transformation I study (i. e. centering and standardizing) is not a bijection : $Z=\alpha{}X$ will have the same image as $X$ (whenever $\alpha\neq{}0$, of course), as well as $T=... | Distribution of a centered standardized sample
Answering to myself : I no longer think that this problem is (directly) solvable, because the transformation I study (i. e. centering and standardizing) is not a bijection : $Z=\alpha{}X$ will have th |
43,574 | Differences between poisson.test and E-test when testing Poisson parameters | The p-value of a hypothesis test or a corresponding confidence interval depends on the treatment or choice of 2 issues:
1. Treatment of nuisance parameter
To preserve the size at the exact level, the type 1 error needs to be less than or equal to alpha for all possible values of the nuisance parameter.
The null hypothe... | Differences between poisson.test and E-test when testing Poisson parameters | The p-value of a hypothesis test or a corresponding confidence interval depends on the treatment or choice of 2 issues:
1. Treatment of nuisance parameter
To preserve the size at the exact level, the | Differences between poisson.test and E-test when testing Poisson parameters
The p-value of a hypothesis test or a corresponding confidence interval depends on the treatment or choice of 2 issues:
1. Treatment of nuisance parameter
To preserve the size at the exact level, the type 1 error needs to be less than or equal ... | Differences between poisson.test and E-test when testing Poisson parameters
The p-value of a hypothesis test or a corresponding confidence interval depends on the treatment or choice of 2 issues:
1. Treatment of nuisance parameter
To preserve the size at the exact level, the |
43,575 | Primitive length measurements | Assuming the only known thing for a combination i,j is whether it is a 1 (iu+e is bigger) or a 0 (otherwise);
Suppose you put the observations in a matrix (I,J) (top left element is 1,1 bottom left is I,1)
Here is the idea that I have. Unfortunately I cannot prove it mathematically, but it might give you some direction... | Primitive length measurements | Assuming the only known thing for a combination i,j is whether it is a 1 (iu+e is bigger) or a 0 (otherwise);
Suppose you put the observations in a matrix (I,J) (top left element is 1,1 bottom left is | Primitive length measurements
Assuming the only known thing for a combination i,j is whether it is a 1 (iu+e is bigger) or a 0 (otherwise);
Suppose you put the observations in a matrix (I,J) (top left element is 1,1 bottom left is I,1)
Here is the idea that I have. Unfortunately I cannot prove it mathematically, but it... | Primitive length measurements
Assuming the only known thing for a combination i,j is whether it is a 1 (iu+e is bigger) or a 0 (otherwise);
Suppose you put the observations in a matrix (I,J) (top left element is 1,1 bottom left is |
43,576 | What exactly is the "wid" argument in the ezANOVA function in the R package "ez"? | First off, are you familiar with R's help system? Typing ?ezANOVA will bring up the documentation for that function, where you can read that the wid argument is a:
.() object specifying the column in data that contains the variable specifying the case/Ss identifier.
Based on how you've specified your model so far, yo... | What exactly is the "wid" argument in the ezANOVA function in the R package "ez"? | First off, are you familiar with R's help system? Typing ?ezANOVA will bring up the documentation for that function, where you can read that the wid argument is a:
.() object specifying the column in | What exactly is the "wid" argument in the ezANOVA function in the R package "ez"?
First off, are you familiar with R's help system? Typing ?ezANOVA will bring up the documentation for that function, where you can read that the wid argument is a:
.() object specifying the column in data that contains the variable speci... | What exactly is the "wid" argument in the ezANOVA function in the R package "ez"?
First off, are you familiar with R's help system? Typing ?ezANOVA will bring up the documentation for that function, where you can read that the wid argument is a:
.() object specifying the column in |
43,577 | Whether to stratify or do a simple random sampling from a set of papers to be compared? | (1) If you can stratify on the delivery method, do so.
(2) If you can stratify on the targets, do so; come up with a meaningful stratification strategy that would give you mutually exclusive categories. (You would have to tell more about how the categories overlap for us to provide meaningful advice.)
(3) If you can st... | Whether to stratify or do a simple random sampling from a set of papers to be compared? | (1) If you can stratify on the delivery method, do so.
(2) If you can stratify on the targets, do so; come up with a meaningful stratification strategy that would give you mutually exclusive categorie | Whether to stratify or do a simple random sampling from a set of papers to be compared?
(1) If you can stratify on the delivery method, do so.
(2) If you can stratify on the targets, do so; come up with a meaningful stratification strategy that would give you mutually exclusive categories. (You would have to tell more ... | Whether to stratify or do a simple random sampling from a set of papers to be compared?
(1) If you can stratify on the delivery method, do so.
(2) If you can stratify on the targets, do so; come up with a meaningful stratification strategy that would give you mutually exclusive categorie |
43,578 | Does it make sense to "cluster" when you use a regression discontinuity? | Whether you take into account clustering or not only affects the standard errors of your estimates. In a situation like yours, I would not focus too much on the standard errors. It is much more important that you can justify the use of the regression discontinuity framework, and you have to be able to show that it allo... | Does it make sense to "cluster" when you use a regression discontinuity? | Whether you take into account clustering or not only affects the standard errors of your estimates. In a situation like yours, I would not focus too much on the standard errors. It is much more import | Does it make sense to "cluster" when you use a regression discontinuity?
Whether you take into account clustering or not only affects the standard errors of your estimates. In a situation like yours, I would not focus too much on the standard errors. It is much more important that you can justify the use of the regress... | Does it make sense to "cluster" when you use a regression discontinuity?
Whether you take into account clustering or not only affects the standard errors of your estimates. In a situation like yours, I would not focus too much on the standard errors. It is much more import |
43,579 | Does it make sense to "cluster" when you use a regression discontinuity? | Is it a regular linear regression model or not?
In Quantitative finance we have the same problem. we use a GARCH model to estimate volatility, however volatility does not seem to be normally distributed and seems to cluster. for this we use a switching garch model. this "switching" model differentiates and switches bet... | Does it make sense to "cluster" when you use a regression discontinuity? | Is it a regular linear regression model or not?
In Quantitative finance we have the same problem. we use a GARCH model to estimate volatility, however volatility does not seem to be normally distribut | Does it make sense to "cluster" when you use a regression discontinuity?
Is it a regular linear regression model or not?
In Quantitative finance we have the same problem. we use a GARCH model to estimate volatility, however volatility does not seem to be normally distributed and seems to cluster. for this we use a swit... | Does it make sense to "cluster" when you use a regression discontinuity?
Is it a regular linear regression model or not?
In Quantitative finance we have the same problem. we use a GARCH model to estimate volatility, however volatility does not seem to be normally distribut |
43,580 | Testing for trends in partial proportional odds models | Unfortunately, I don't know the partial proportional odds model. I do know that Agresti--discussing other situations (eg, PO)--has suggested that people go ahead & make up values for your categories and run them as though they were continuous. I find this somewhat discomfiting, but his argument is that unless you're wa... | Testing for trends in partial proportional odds models | Unfortunately, I don't know the partial proportional odds model. I do know that Agresti--discussing other situations (eg, PO)--has suggested that people go ahead & make up values for your categories a | Testing for trends in partial proportional odds models
Unfortunately, I don't know the partial proportional odds model. I do know that Agresti--discussing other situations (eg, PO)--has suggested that people go ahead & make up values for your categories and run them as though they were continuous. I find this somewhat ... | Testing for trends in partial proportional odds models
Unfortunately, I don't know the partial proportional odds model. I do know that Agresti--discussing other situations (eg, PO)--has suggested that people go ahead & make up values for your categories a |
43,581 | Testing for trends in partial proportional odds models | I think this is a reasonable way of assessing trend. The key boils down to the interpretation of the coefficient or effect that is estimated by the model. What you estimate is, in effect, an odds ratio for endorsing a unit higher $Y$ response comparing groups differing by 1 unit in $V$.
The proportional odds model usua... | Testing for trends in partial proportional odds models | I think this is a reasonable way of assessing trend. The key boils down to the interpretation of the coefficient or effect that is estimated by the model. What you estimate is, in effect, an odds rati | Testing for trends in partial proportional odds models
I think this is a reasonable way of assessing trend. The key boils down to the interpretation of the coefficient or effect that is estimated by the model. What you estimate is, in effect, an odds ratio for endorsing a unit higher $Y$ response comparing groups diffe... | Testing for trends in partial proportional odds models
I think this is a reasonable way of assessing trend. The key boils down to the interpretation of the coefficient or effect that is estimated by the model. What you estimate is, in effect, an odds rati |
43,582 | How to handle changing definitions of regions over time in data? | I may be wrong, but why don't you forecast each store by itself and then aggregate them to the current "major break". | How to handle changing definitions of regions over time in data? | I may be wrong, but why don't you forecast each store by itself and then aggregate them to the current "major break". | How to handle changing definitions of regions over time in data?
I may be wrong, but why don't you forecast each store by itself and then aggregate them to the current "major break". | How to handle changing definitions of regions over time in data?
I may be wrong, but why don't you forecast each store by itself and then aggregate them to the current "major break". |
43,583 | Is there a way to specify a lme model with more than one within-subjects factor? | I found an answer to my question on this thread: Repeated measures ANOVA with lme in R for two within-subject factors (somehow this thread was already one of my favorites, I must have forgotten about it). The specification is a little unhandy.
m6 <- lme(mean ~ condition*group*problem*topic,
random = list(code=pdBlo... | Is there a way to specify a lme model with more than one within-subjects factor? | I found an answer to my question on this thread: Repeated measures ANOVA with lme in R for two within-subject factors (somehow this thread was already one of my favorites, I must have forgotten about | Is there a way to specify a lme model with more than one within-subjects factor?
I found an answer to my question on this thread: Repeated measures ANOVA with lme in R for two within-subject factors (somehow this thread was already one of my favorites, I must have forgotten about it). The specification is a little unha... | Is there a way to specify a lme model with more than one within-subjects factor?
I found an answer to my question on this thread: Repeated measures ANOVA with lme in R for two within-subject factors (somehow this thread was already one of my favorites, I must have forgotten about |
43,584 | Find the minimum of an expensive-to-sample noisy paraboloid | One approach is to reparametrize your model, revealing a completely boring OLS regression. You can then find the covariance of the parameters as a function of $z$, convert it back to get the covariance of $x_{min}$, and optimize some function of it.
Reparameterizing
If $y_i = (x_i^T-x_{min})^Ta(x_i^T-x_{min}) + c + \ep... | Find the minimum of an expensive-to-sample noisy paraboloid | One approach is to reparametrize your model, revealing a completely boring OLS regression. You can then find the covariance of the parameters as a function of $z$, convert it back to get the covarianc | Find the minimum of an expensive-to-sample noisy paraboloid
One approach is to reparametrize your model, revealing a completely boring OLS regression. You can then find the covariance of the parameters as a function of $z$, convert it back to get the covariance of $x_{min}$, and optimize some function of it.
Reparamete... | Find the minimum of an expensive-to-sample noisy paraboloid
One approach is to reparametrize your model, revealing a completely boring OLS regression. You can then find the covariance of the parameters as a function of $z$, convert it back to get the covarianc |
43,585 | Are two empirically estimated Markov chains statistically different? | Since the two chains are assumed to be comparable, they should have the same state space. That leaves the transition matrices, comparing which can be done by a divergence-based hypothesis test, as explained on pg. 139 of Statistical inference based on divergence measures By Leandro Pardo Llorente | Are two empirically estimated Markov chains statistically different? | Since the two chains are assumed to be comparable, they should have the same state space. That leaves the transition matrices, comparing which can be done by a divergence-based hypothesis test, as exp | Are two empirically estimated Markov chains statistically different?
Since the two chains are assumed to be comparable, they should have the same state space. That leaves the transition matrices, comparing which can be done by a divergence-based hypothesis test, as explained on pg. 139 of Statistical inference based on... | Are two empirically estimated Markov chains statistically different?
Since the two chains are assumed to be comparable, they should have the same state space. That leaves the transition matrices, comparing which can be done by a divergence-based hypothesis test, as exp |
43,586 | Are two empirically estimated Markov chains statistically different? | Here's half-baked idea. Please tell me why it's wrong. :)
Choose randomly a state sequence from dataset A, and leave it out when constructing the chain for that dataset.
Construct the chains for datasets A and B.
Run the sequence through chains A and B, and record the predicted final state.
Repeat 1-3 lots of times.
R... | Are two empirically estimated Markov chains statistically different? | Here's half-baked idea. Please tell me why it's wrong. :)
Choose randomly a state sequence from dataset A, and leave it out when constructing the chain for that dataset.
Construct the chains for data | Are two empirically estimated Markov chains statistically different?
Here's half-baked idea. Please tell me why it's wrong. :)
Choose randomly a state sequence from dataset A, and leave it out when constructing the chain for that dataset.
Construct the chains for datasets A and B.
Run the sequence through chains A and... | Are two empirically estimated Markov chains statistically different?
Here's half-baked idea. Please tell me why it's wrong. :)
Choose randomly a state sequence from dataset A, and leave it out when constructing the chain for that dataset.
Construct the chains for data |
43,587 | How to calculate standard errors in OLS without inverting the X'X matrix? | I had the same problem as I wanted to use the most efficient solvers available in my econometrics package. I developed an algorithm to solve for both the $\beta$ and the inverse of the linear predictor (normal matrix) for linear least squares (it also applies to WLS, Ridge regression, etc.)
So here is the pseudo-code,
... | How to calculate standard errors in OLS without inverting the X'X matrix? | I had the same problem as I wanted to use the most efficient solvers available in my econometrics package. I developed an algorithm to solve for both the $\beta$ and the inverse of the linear predicto | How to calculate standard errors in OLS without inverting the X'X matrix?
I had the same problem as I wanted to use the most efficient solvers available in my econometrics package. I developed an algorithm to solve for both the $\beta$ and the inverse of the linear predictor (normal matrix) for linear least squares (it... | How to calculate standard errors in OLS without inverting the X'X matrix?
I had the same problem as I wanted to use the most efficient solvers available in my econometrics package. I developed an algorithm to solve for both the $\beta$ and the inverse of the linear predicto |
43,588 | Is there a way to compare linear regression slopes by permutation tests? | In principle, you can use a permutation test on any function on data of two groups. To compare regression slopes, you simply pick to species, shuffle the data points between the groups randomly, but instead of comparing the mean value of each permuted group, compare the regression slope. You may have to center your dat... | Is there a way to compare linear regression slopes by permutation tests? | In principle, you can use a permutation test on any function on data of two groups. To compare regression slopes, you simply pick to species, shuffle the data points between the groups randomly, but i | Is there a way to compare linear regression slopes by permutation tests?
In principle, you can use a permutation test on any function on data of two groups. To compare regression slopes, you simply pick to species, shuffle the data points between the groups randomly, but instead of comparing the mean value of each perm... | Is there a way to compare linear regression slopes by permutation tests?
In principle, you can use a permutation test on any function on data of two groups. To compare regression slopes, you simply pick to species, shuffle the data points between the groups randomly, but i |
43,589 | Large number of correlations with MRI region of interest (ROI) variables: What adjustment should I perform? | The answer depends on the type of inference you wish to make. Do you wish to make statements about each ROI or do you want to quantify the distribution of effect over ROIs?
If you wish to infer on each ROI, multiplicity correction is the way to go. The fact that you do not need to define the exact dependence between th... | Large number of correlations with MRI region of interest (ROI) variables: What adjustment should I p | The answer depends on the type of inference you wish to make. Do you wish to make statements about each ROI or do you want to quantify the distribution of effect over ROIs?
If you wish to infer on eac | Large number of correlations with MRI region of interest (ROI) variables: What adjustment should I perform?
The answer depends on the type of inference you wish to make. Do you wish to make statements about each ROI or do you want to quantify the distribution of effect over ROIs?
If you wish to infer on each ROI, multi... | Large number of correlations with MRI region of interest (ROI) variables: What adjustment should I p
The answer depends on the type of inference you wish to make. Do you wish to make statements about each ROI or do you want to quantify the distribution of effect over ROIs?
If you wish to infer on eac |
43,590 | Large number of correlations with MRI region of interest (ROI) variables: What adjustment should I perform? | In case your approach is not look at the volume of a particular ROI but to "screen through" the whole brain to look for differences associated with cannabis use, maybe running voxel-based morphometry (http://dbm.neuro.uni-jena.de/vbm/) would be a more sensitive approach? | Large number of correlations with MRI region of interest (ROI) variables: What adjustment should I p | In case your approach is not look at the volume of a particular ROI but to "screen through" the whole brain to look for differences associated with cannabis use, maybe running voxel-based morphometry | Large number of correlations with MRI region of interest (ROI) variables: What adjustment should I perform?
In case your approach is not look at the volume of a particular ROI but to "screen through" the whole brain to look for differences associated with cannabis use, maybe running voxel-based morphometry (http://dbm.... | Large number of correlations with MRI region of interest (ROI) variables: What adjustment should I p
In case your approach is not look at the volume of a particular ROI but to "screen through" the whole brain to look for differences associated with cannabis use, maybe running voxel-based morphometry |
43,591 | What are the top statistic conferences to follow for applications in machine learning? | The Joint Statistical Meetings are large annual statistical conferences. To quote their site,
"JSM (the Joint Statistical Meetings) is the largest gathering of
statisticians held in North America. It is held jointly with the
American Statistical Association, the International Biometric Society
(ENAR and WNAR),... | What are the top statistic conferences to follow for applications in machine learning? | The Joint Statistical Meetings are large annual statistical conferences. To quote their site,
"JSM (the Joint Statistical Meetings) is the largest gathering of
statisticians held in North America | What are the top statistic conferences to follow for applications in machine learning?
The Joint Statistical Meetings are large annual statistical conferences. To quote their site,
"JSM (the Joint Statistical Meetings) is the largest gathering of
statisticians held in North America. It is held jointly with the
A... | What are the top statistic conferences to follow for applications in machine learning?
The Joint Statistical Meetings are large annual statistical conferences. To quote their site,
"JSM (the Joint Statistical Meetings) is the largest gathering of
statisticians held in North America |
43,592 | Comparison of areas under curves | While my comment that ideally more information should be given holds, if this really is all you have to go on you could at least say something by performing a permutation test. In other words: calculate AUC1 - AUC2 for all random re-labelings of gender and see where the value you actually observed falls within that dis... | Comparison of areas under curves | While my comment that ideally more information should be given holds, if this really is all you have to go on you could at least say something by performing a permutation test. In other words: calcula | Comparison of areas under curves
While my comment that ideally more information should be given holds, if this really is all you have to go on you could at least say something by performing a permutation test. In other words: calculate AUC1 - AUC2 for all random re-labelings of gender and see where the value you actual... | Comparison of areas under curves
While my comment that ideally more information should be given holds, if this really is all you have to go on you could at least say something by performing a permutation test. In other words: calcula |
43,593 | Two-way unsupervised learning | You might want to have a look at these papers:
F. Bach, M. I. Jordan. A probabilistic interpretation of canonical correlation analysis. Technical Report 688, Department of Statistics, University of California, Berkeley, 2005
and
Cédric Archambeau, Nicolas Delannay, and Michel Verleysen. 2006. Robust probabilistic proje... | Two-way unsupervised learning | You might want to have a look at these papers:
F. Bach, M. I. Jordan. A probabilistic interpretation of canonical correlation analysis. Technical Report 688, Department of Statistics, University of Ca | Two-way unsupervised learning
You might want to have a look at these papers:
F. Bach, M. I. Jordan. A probabilistic interpretation of canonical correlation analysis. Technical Report 688, Department of Statistics, University of California, Berkeley, 2005
and
Cédric Archambeau, Nicolas Delannay, and Michel Verleysen. 20... | Two-way unsupervised learning
You might want to have a look at these papers:
F. Bach, M. I. Jordan. A probabilistic interpretation of canonical correlation analysis. Technical Report 688, Department of Statistics, University of Ca |
43,594 | How do I adjust standard errors in a research study in which the control group is constructed via matching with replacement? | Frankly, I don't think there is any justifiable way to compute the standard errors. The bootstrap appears to be a commonly used method, but Abadie and Imbens (2008 Econometrica) paper demonstrated the problems the bootstrap runs into. For a method as complicated as PS matching, I am not entirely sure 100 is a sufficien... | How do I adjust standard errors in a research study in which the control group is constructed via ma | Frankly, I don't think there is any justifiable way to compute the standard errors. The bootstrap appears to be a commonly used method, but Abadie and Imbens (2008 Econometrica) paper demonstrated the | How do I adjust standard errors in a research study in which the control group is constructed via matching with replacement?
Frankly, I don't think there is any justifiable way to compute the standard errors. The bootstrap appears to be a commonly used method, but Abadie and Imbens (2008 Econometrica) paper demonstrate... | How do I adjust standard errors in a research study in which the control group is constructed via ma
Frankly, I don't think there is any justifiable way to compute the standard errors. The bootstrap appears to be a commonly used method, but Abadie and Imbens (2008 Econometrica) paper demonstrated the |
43,595 | How to get the correlation between two large (sparse) matrices? | You can simply take random samples (i.e. submatrices) and estimate the Mantel test statistic. With enough samples, this should converge on the same conclusion. In this way, you can avoid having to cook up a method for accessing all of the data.
However, as these are sparse matrices, it's better to go after a method f... | How to get the correlation between two large (sparse) matrices? | You can simply take random samples (i.e. submatrices) and estimate the Mantel test statistic. With enough samples, this should converge on the same conclusion. In this way, you can avoid having to c | How to get the correlation between two large (sparse) matrices?
You can simply take random samples (i.e. submatrices) and estimate the Mantel test statistic. With enough samples, this should converge on the same conclusion. In this way, you can avoid having to cook up a method for accessing all of the data.
However, ... | How to get the correlation between two large (sparse) matrices?
You can simply take random samples (i.e. submatrices) and estimate the Mantel test statistic. With enough samples, this should converge on the same conclusion. In this way, you can avoid having to c |
43,596 | Selecting regression model for a non-negative integer response | Your model is fully saturated, because you have indicators for every possible combination of categories. As such you have correctly specified the conditional expectation.
Any MLE estimate based on a distribution in the linear exponential family is consistent when the conditional expectation is correctly specified. Ther... | Selecting regression model for a non-negative integer response | Your model is fully saturated, because you have indicators for every possible combination of categories. As such you have correctly specified the conditional expectation.
Any MLE estimate based on a d | Selecting regression model for a non-negative integer response
Your model is fully saturated, because you have indicators for every possible combination of categories. As such you have correctly specified the conditional expectation.
Any MLE estimate based on a distribution in the linear exponential family is consisten... | Selecting regression model for a non-negative integer response
Your model is fully saturated, because you have indicators for every possible combination of categories. As such you have correctly specified the conditional expectation.
Any MLE estimate based on a d |
43,597 | What does inverse-chi-square in Fisher method (classifying) exactly do? | This document extensively answers your question :
Why Chi?, Motivations for the Use of Fisher's Inverse Chi-Square Procedure in Spam Classification, by Gary Robinson | What does inverse-chi-square in Fisher method (classifying) exactly do? | This document extensively answers your question :
Why Chi?, Motivations for the Use of Fisher's Inverse Chi-Square Procedure in Spam Classification, by Gary Robinson | What does inverse-chi-square in Fisher method (classifying) exactly do?
This document extensively answers your question :
Why Chi?, Motivations for the Use of Fisher's Inverse Chi-Square Procedure in Spam Classification, by Gary Robinson | What does inverse-chi-square in Fisher method (classifying) exactly do?
This document extensively answers your question :
Why Chi?, Motivations for the Use of Fisher's Inverse Chi-Square Procedure in Spam Classification, by Gary Robinson |
43,598 | Distribution family for a ratio dependent variable in a generalized estimating equation | maximum entropy is a good way to go here. With maximum entropy, you specify the "structure" that your model is to depend on, and it does the rest. It has a very similar form to a generalised estimating equation. So we have an unknown (or "random") variable $x$ that is the object of inference (may be a vector). It c... | Distribution family for a ratio dependent variable in a generalized estimating equation | maximum entropy is a good way to go here. With maximum entropy, you specify the "structure" that your model is to depend on, and it does the rest. It has a very similar form to a generalised estimat | Distribution family for a ratio dependent variable in a generalized estimating equation
maximum entropy is a good way to go here. With maximum entropy, you specify the "structure" that your model is to depend on, and it does the rest. It has a very similar form to a generalised estimating equation. So we have an unk... | Distribution family for a ratio dependent variable in a generalized estimating equation
maximum entropy is a good way to go here. With maximum entropy, you specify the "structure" that your model is to depend on, and it does the rest. It has a very similar form to a generalised estimat |
43,599 | Minimal number of samples/conversions for statistical validity | I am not be able to answer your question completely, but are you looking for this:
Power analysis - http://www.statmethods.net/stats/power.html | Minimal number of samples/conversions for statistical validity | I am not be able to answer your question completely, but are you looking for this:
Power analysis - http://www.statmethods.net/stats/power.html | Minimal number of samples/conversions for statistical validity
I am not be able to answer your question completely, but are you looking for this:
Power analysis - http://www.statmethods.net/stats/power.html | Minimal number of samples/conversions for statistical validity
I am not be able to answer your question completely, but are you looking for this:
Power analysis - http://www.statmethods.net/stats/power.html |
43,600 | What is the default covariance structure in glmer and can I change it? | I do not know about SAS, but variance in glmer is controlled by family argument. If you want to change correlation structure then I suspect you will have to use nlme from nlme package. | What is the default covariance structure in glmer and can I change it? | I do not know about SAS, but variance in glmer is controlled by family argument. If you want to change correlation structure then I suspect you will have to use nlme from nlme package. | What is the default covariance structure in glmer and can I change it?
I do not know about SAS, but variance in glmer is controlled by family argument. If you want to change correlation structure then I suspect you will have to use nlme from nlme package. | What is the default covariance structure in glmer and can I change it?
I do not know about SAS, but variance in glmer is controlled by family argument. If you want to change correlation structure then I suspect you will have to use nlme from nlme package. |
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