idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
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36,101 | When has a Bayesian approach been critical to addressing a theory, hypothesis or problem? | In response to my own question, an article was just published in the journal Ecology titled "Density estimation in tiger populations: combining information for strong inference" by Gopalaswamy et al. They used a Bayesian model that combined information from tiger studies with different methodologies to improve the acc... | When has a Bayesian approach been critical to addressing a theory, hypothesis or problem? | In response to my own question, an article was just published in the journal Ecology titled "Density estimation in tiger populations: combining information for strong inference" by Gopalaswamy et al. | When has a Bayesian approach been critical to addressing a theory, hypothesis or problem?
In response to my own question, an article was just published in the journal Ecology titled "Density estimation in tiger populations: combining information for strong inference" by Gopalaswamy et al. They used a Bayesian model th... | When has a Bayesian approach been critical to addressing a theory, hypothesis or problem?
In response to my own question, an article was just published in the journal Ecology titled "Density estimation in tiger populations: combining information for strong inference" by Gopalaswamy et al. |
36,102 | Common weak learners for Adaboost | The most basic and most common weak learner is a decision stump which is basically a single level decision tree. That means, say the points in your dataset are N dimensional (features), a decision stump is a threshold on a single dimension. Below the threshold is one class, above it the other.
Pretty much any classifi... | Common weak learners for Adaboost | The most basic and most common weak learner is a decision stump which is basically a single level decision tree. That means, say the points in your dataset are N dimensional (features), a decision stu | Common weak learners for Adaboost
The most basic and most common weak learner is a decision stump which is basically a single level decision tree. That means, say the points in your dataset are N dimensional (features), a decision stump is a threshold on a single dimension. Below the threshold is one class, above it th... | Common weak learners for Adaboost
The most basic and most common weak learner is a decision stump which is basically a single level decision tree. That means, say the points in your dataset are N dimensional (features), a decision stu |
36,103 | Is this an unbiased estimator for standard deviation of normal distribution? | The proposed estimator is not unbiased, at least if we indeed know the true mean, $\mu$, and if we are dealing with a normal sample as the title says, where the distribution is symmetric and unimodal and the mean equals the median. Informally, knowing the true mean, makes the mean absolute deviation equal in value to t... | Is this an unbiased estimator for standard deviation of normal distribution? | The proposed estimator is not unbiased, at least if we indeed know the true mean, $\mu$, and if we are dealing with a normal sample as the title says, where the distribution is symmetric and unimodal | Is this an unbiased estimator for standard deviation of normal distribution?
The proposed estimator is not unbiased, at least if we indeed know the true mean, $\mu$, and if we are dealing with a normal sample as the title says, where the distribution is symmetric and unimodal and the mean equals the median. Informally,... | Is this an unbiased estimator for standard deviation of normal distribution?
The proposed estimator is not unbiased, at least if we indeed know the true mean, $\mu$, and if we are dealing with a normal sample as the title says, where the distribution is symmetric and unimodal |
36,104 | When is a covariance `degenerate in some direction`? | Yes.
This figure shows the situation for $p=2$ where the span of $\{\mathbf{x}_1, \ldots, \mathbf{x}_K\}$ is one-dimensional, shown as a red line through the origin, and the orthogonal space--the kernel of the covariance matrix--also is one-dimensional, shown as a dashed gray line through the origin. Data are shown a... | When is a covariance `degenerate in some direction`? | Yes.
This figure shows the situation for $p=2$ where the span of $\{\mathbf{x}_1, \ldots, \mathbf{x}_K\}$ is one-dimensional, shown as a red line through the origin, and the orthogonal space--the ker | When is a covariance `degenerate in some direction`?
Yes.
This figure shows the situation for $p=2$ where the span of $\{\mathbf{x}_1, \ldots, \mathbf{x}_K\}$ is one-dimensional, shown as a red line through the origin, and the orthogonal space--the kernel of the covariance matrix--also is one-dimensional, shown as a d... | When is a covariance `degenerate in some direction`?
Yes.
This figure shows the situation for $p=2$ where the span of $\{\mathbf{x}_1, \ldots, \mathbf{x}_K\}$ is one-dimensional, shown as a red line through the origin, and the orthogonal space--the ker |
36,105 | When is a covariance `degenerate in some direction`? | In this case the covariance matrix is singular, i.e. it is not of full rank. The directions associated with the zero eigenvalues are degenerate. | When is a covariance `degenerate in some direction`? | In this case the covariance matrix is singular, i.e. it is not of full rank. The directions associated with the zero eigenvalues are degenerate. | When is a covariance `degenerate in some direction`?
In this case the covariance matrix is singular, i.e. it is not of full rank. The directions associated with the zero eigenvalues are degenerate. | When is a covariance `degenerate in some direction`?
In this case the covariance matrix is singular, i.e. it is not of full rank. The directions associated with the zero eigenvalues are degenerate. |
36,106 | Estimating prediction error | If you have done cross-validation very carefully (there are many ways to make mistakes that can lead to overly optimistic results) then if your new data is drawn from the same population as the training data, the cross-validation result should be about right. In technical terms cross-validation should return an unbiase... | Estimating prediction error | If you have done cross-validation very carefully (there are many ways to make mistakes that can lead to overly optimistic results) then if your new data is drawn from the same population as the traini | Estimating prediction error
If you have done cross-validation very carefully (there are many ways to make mistakes that can lead to overly optimistic results) then if your new data is drawn from the same population as the training data, the cross-validation result should be about right. In technical terms cross-validat... | Estimating prediction error
If you have done cross-validation very carefully (there are many ways to make mistakes that can lead to overly optimistic results) then if your new data is drawn from the same population as the traini |
36,107 | Estimating prediction error | Let me add to Bogdanovist's excellent answer that
cross validation is unbiased for what it measures: the predicitive abilities of "surrogate" models with respect to the data at hand ("drawn from the same population").
the often stated pessimistic bias arises in situations where the surrogate models are on average wors... | Estimating prediction error | Let me add to Bogdanovist's excellent answer that
cross validation is unbiased for what it measures: the predicitive abilities of "surrogate" models with respect to the data at hand ("drawn from the | Estimating prediction error
Let me add to Bogdanovist's excellent answer that
cross validation is unbiased for what it measures: the predicitive abilities of "surrogate" models with respect to the data at hand ("drawn from the same population").
the often stated pessimistic bias arises in situations where the surrogat... | Estimating prediction error
Let me add to Bogdanovist's excellent answer that
cross validation is unbiased for what it measures: the predicitive abilities of "surrogate" models with respect to the data at hand ("drawn from the |
36,108 | Are there references for plotting binary time series? | Kedem and Fokianos in their book "Regression Models for Time Series Analysis" have a whole chapter (Chapter 2) on binary time series models with many examples of plotted series and periodograms.
In response to whuber's request I am adding some description of the plots in the chapter.
page 63 Fig 2.3 This figure is in t... | Are there references for plotting binary time series? | Kedem and Fokianos in their book "Regression Models for Time Series Analysis" have a whole chapter (Chapter 2) on binary time series models with many examples of plotted series and periodograms.
In re | Are there references for plotting binary time series?
Kedem and Fokianos in their book "Regression Models for Time Series Analysis" have a whole chapter (Chapter 2) on binary time series models with many examples of plotted series and periodograms.
In response to whuber's request I am adding some description of the plo... | Are there references for plotting binary time series?
Kedem and Fokianos in their book "Regression Models for Time Series Analysis" have a whole chapter (Chapter 2) on binary time series models with many examples of plotted series and periodograms.
In re |
36,109 | Are there references for plotting binary time series? | As always, it depends on the purpose of the plot: what is it intended to communicate to whom? In any event, cumulative plots tend to be interesting and informative. The NY Times has lately been producing many nice examples. Some examples of similar plots appear on the "Edward Tufte forum". This combination of "spark... | Are there references for plotting binary time series? | As always, it depends on the purpose of the plot: what is it intended to communicate to whom? In any event, cumulative plots tend to be interesting and informative. The NY Times has lately been prod | Are there references for plotting binary time series?
As always, it depends on the purpose of the plot: what is it intended to communicate to whom? In any event, cumulative plots tend to be interesting and informative. The NY Times has lately been producing many nice examples. Some examples of similar plots appear on... | Are there references for plotting binary time series?
As always, it depends on the purpose of the plot: what is it intended to communicate to whom? In any event, cumulative plots tend to be interesting and informative. The NY Times has lately been prod |
36,110 | Are there references for plotting binary time series? | Just to follow up on this issue, I didn't find any other resources on plotting binary series and wound up going with the original line-plots that I dismissed initially. (The plots of the observed series in the book M. Chernick refers to also plot the original data just as lines, which I discovered after making my choic... | Are there references for plotting binary time series? | Just to follow up on this issue, I didn't find any other resources on plotting binary series and wound up going with the original line-plots that I dismissed initially. (The plots of the observed seri | Are there references for plotting binary time series?
Just to follow up on this issue, I didn't find any other resources on plotting binary series and wound up going with the original line-plots that I dismissed initially. (The plots of the observed series in the book M. Chernick refers to also plot the original data j... | Are there references for plotting binary time series?
Just to follow up on this issue, I didn't find any other resources on plotting binary series and wound up going with the original line-plots that I dismissed initially. (The plots of the observed seri |
36,111 | Online reference for review of introductory statistics material | Take a look at these documents:
http://onlinestatbook.com/Online_Statistics_Education.pdf
http://www.micquality.com/downloads/ref-primer.pdf
And at this site, for more materials:
http://onlinestatbook.com/
Hope this helps. | Online reference for review of introductory statistics material | Take a look at these documents:
http://onlinestatbook.com/Online_Statistics_Education.pdf
http://www.micquality.com/downloads/ref-primer.pdf
And at this site, for more materials:
http://onlinestatbook | Online reference for review of introductory statistics material
Take a look at these documents:
http://onlinestatbook.com/Online_Statistics_Education.pdf
http://www.micquality.com/downloads/ref-primer.pdf
And at this site, for more materials:
http://onlinestatbook.com/
Hope this helps. | Online reference for review of introductory statistics material
Take a look at these documents:
http://onlinestatbook.com/Online_Statistics_Education.pdf
http://www.micquality.com/downloads/ref-primer.pdf
And at this site, for more materials:
http://onlinestatbook |
36,112 | Online reference for review of introductory statistics material | These are not PDFs, but there are quite a few good videos at the Khan Academy. | Online reference for review of introductory statistics material | These are not PDFs, but there are quite a few good videos at the Khan Academy. | Online reference for review of introductory statistics material
These are not PDFs, but there are quite a few good videos at the Khan Academy. | Online reference for review of introductory statistics material
These are not PDFs, but there are quite a few good videos at the Khan Academy. |
36,113 | Online reference for review of introductory statistics material | There are so many good possibilities and your vague description makes it difficult to narrow it down to just a couple.
But here is a short list.
1. Humourous but also clear and accurate by Gonick "The Cartoon Guide to Statistics 1993"
http://www.amazon.com/Cartoon-Guide-Statistics-Larry-Gonick/dp/0062731025/ref=sr_1_1... | Online reference for review of introductory statistics material | There are so many good possibilities and your vague description makes it difficult to narrow it down to just a couple.
But here is a short list.
1. Humourous but also clear and accurate by Gonick "Th | Online reference for review of introductory statistics material
There are so many good possibilities and your vague description makes it difficult to narrow it down to just a couple.
But here is a short list.
1. Humourous but also clear and accurate by Gonick "The Cartoon Guide to Statistics 1993"
http://www.amazon.co... | Online reference for review of introductory statistics material
There are so many good possibilities and your vague description makes it difficult to narrow it down to just a couple.
But here is a short list.
1. Humourous but also clear and accurate by Gonick "Th |
36,114 | Online reference for review of introductory statistics material | I think these two free PDF's are very good for this purpose:
This one is a more "conceptual" introduction, good for a refresher:
http://www.stat-help.com/intro.pdf
And this one is a more "complete" introduction:
http://www.openintro.org/stat/down/OpenIntroStatFirst.pdf | Online reference for review of introductory statistics material | I think these two free PDF's are very good for this purpose:
This one is a more "conceptual" introduction, good for a refresher:
http://www.stat-help.com/intro.pdf
And this one is a more "complete" i | Online reference for review of introductory statistics material
I think these two free PDF's are very good for this purpose:
This one is a more "conceptual" introduction, good for a refresher:
http://www.stat-help.com/intro.pdf
And this one is a more "complete" introduction:
http://www.openintro.org/stat/down/OpenIntr... | Online reference for review of introductory statistics material
I think these two free PDF's are very good for this purpose:
This one is a more "conceptual" introduction, good for a refresher:
http://www.stat-help.com/intro.pdf
And this one is a more "complete" i |
36,115 | Online reference for review of introductory statistics material | David Colquhoun's book "Lectures on Biostatistics" covers most of the material that you mention and is available as a free pdf from the author's website http://www.dcscience.net/
It is slightly idiosyncratic in parts (which will not surprise any who know the author) and quite entertaining (the test for pureness of hear... | Online reference for review of introductory statistics material | David Colquhoun's book "Lectures on Biostatistics" covers most of the material that you mention and is available as a free pdf from the author's website http://www.dcscience.net/
It is slightly idiosy | Online reference for review of introductory statistics material
David Colquhoun's book "Lectures on Biostatistics" covers most of the material that you mention and is available as a free pdf from the author's website http://www.dcscience.net/
It is slightly idiosyncratic in parts (which will not surprise any who know t... | Online reference for review of introductory statistics material
David Colquhoun's book "Lectures on Biostatistics" covers most of the material that you mention and is available as a free pdf from the author's website http://www.dcscience.net/
It is slightly idiosy |
36,116 | Online reference for review of introductory statistics material | The NIST/SEMATECH e-Handbook of Statistical Methods, also known as the Engineering Statistics Handbook is a great, and authoritative, reference. It is continuously supported by the National Institute of Standards and Technology (U.S. tax dollars at work!)
It is available in pdf at http://www.itl.nist.gov/div898/handboo... | Online reference for review of introductory statistics material | The NIST/SEMATECH e-Handbook of Statistical Methods, also known as the Engineering Statistics Handbook is a great, and authoritative, reference. It is continuously supported by the National Institute | Online reference for review of introductory statistics material
The NIST/SEMATECH e-Handbook of Statistical Methods, also known as the Engineering Statistics Handbook is a great, and authoritative, reference. It is continuously supported by the National Institute of Standards and Technology (U.S. tax dollars at work!)
... | Online reference for review of introductory statistics material
The NIST/SEMATECH e-Handbook of Statistical Methods, also known as the Engineering Statistics Handbook is a great, and authoritative, reference. It is continuously supported by the National Institute |
36,117 | What is a stationary Gaussian field? | For time series stationarity means that the joint distribution of variables in the sequence depends only on their separation in time and not on the actual time. This implies that the mean and variance are constant and the covariance between the variables at two time points depends only on the difference in time between... | What is a stationary Gaussian field? | For time series stationarity means that the joint distribution of variables in the sequence depends only on their separation in time and not on the actual time. This implies that the mean and variance | What is a stationary Gaussian field?
For time series stationarity means that the joint distribution of variables in the sequence depends only on their separation in time and not on the actual time. This implies that the mean and variance are constant and the covariance between the variables at two time points depends o... | What is a stationary Gaussian field?
For time series stationarity means that the joint distribution of variables in the sequence depends only on their separation in time and not on the actual time. This implies that the mean and variance |
36,118 | Understanding the linear mixed effects model equation and fitting a random effects model with weights in R | I'll answer each of your questions one at a time.
May I ask if the following model is a random-intercept model?
1. There is a common beta for all N individuals.
2. Each group has a different within group regression line (same slope but different intercepts).
3. The regression line within each group crosses the "cloud" ... | Understanding the linear mixed effects model equation and fitting a random effects model with weight | I'll answer each of your questions one at a time.
May I ask if the following model is a random-intercept model?
1. There is a common beta for all N individuals.
2. Each group has a different within gr | Understanding the linear mixed effects model equation and fitting a random effects model with weights in R
I'll answer each of your questions one at a time.
May I ask if the following model is a random-intercept model?
1. There is a common beta for all N individuals.
2. Each group has a different within group regressio... | Understanding the linear mixed effects model equation and fitting a random effects model with weight
I'll answer each of your questions one at a time.
May I ask if the following model is a random-intercept model?
1. There is a common beta for all N individuals.
2. Each group has a different within gr |
36,119 | Comparing two classifiers on separate pairs of train and test datasets | First of all, before testing you need to define couple of things: do all classification errors have same "cost"? Then you chose a single measurement parameter. I usually chose MCC for binary data and Cohen's kappa for k-category classification. Next it is very important to define what is the minimal difference that is ... | Comparing two classifiers on separate pairs of train and test datasets | First of all, before testing you need to define couple of things: do all classification errors have same "cost"? Then you chose a single measurement parameter. I usually chose MCC for binary data and | Comparing two classifiers on separate pairs of train and test datasets
First of all, before testing you need to define couple of things: do all classification errors have same "cost"? Then you chose a single measurement parameter. I usually chose MCC for binary data and Cohen's kappa for k-category classification. Next... | Comparing two classifiers on separate pairs of train and test datasets
First of all, before testing you need to define couple of things: do all classification errors have same "cost"? Then you chose a single measurement parameter. I usually chose MCC for binary data and |
36,120 | The only spherical and independent density is normal! | This is a standard calculus derivation: spherical symmetry tells you that $f_1(x)$ is a function of $x^2$, i.e.
$$f_1(x)=g_1(x^2).$$ Independence plus spherical symmetry tell you that
$$g_1(u)g_1(0)=g_2(u) \quad\text{and}\quad g_1(u)g_1(v)=g_2(u+v)\propto g_1(u+v)$$
Therefore, rescaling $g_1$ into $h_1$ so that the ab... | The only spherical and independent density is normal! | This is a standard calculus derivation: spherical symmetry tells you that $f_1(x)$ is a function of $x^2$, i.e.
$$f_1(x)=g_1(x^2).$$ Independence plus spherical symmetry tell you that
$$g_1(u)g_1(0)= | The only spherical and independent density is normal!
This is a standard calculus derivation: spherical symmetry tells you that $f_1(x)$ is a function of $x^2$, i.e.
$$f_1(x)=g_1(x^2).$$ Independence plus spherical symmetry tell you that
$$g_1(u)g_1(0)=g_2(u) \quad\text{and}\quad g_1(u)g_1(v)=g_2(u+v)\propto g_1(u+v)$... | The only spherical and independent density is normal!
This is a standard calculus derivation: spherical symmetry tells you that $f_1(x)$ is a function of $x^2$, i.e.
$$f_1(x)=g_1(x^2).$$ Independence plus spherical symmetry tell you that
$$g_1(u)g_1(0)= |
36,121 | error in getting predictions from a lme object | Thanks for providing the data so that I could perform some diagnostics.
Actually, this is an epic bug of predict.lme. Your factors have more levels in your initial data (for example you have more than 4 countries) than in your new data. A line of code specifically causes the unused levels to be discarded so you end up ... | error in getting predictions from a lme object | Thanks for providing the data so that I could perform some diagnostics.
Actually, this is an epic bug of predict.lme. Your factors have more levels in your initial data (for example you have more than | error in getting predictions from a lme object
Thanks for providing the data so that I could perform some diagnostics.
Actually, this is an epic bug of predict.lme. Your factors have more levels in your initial data (for example you have more than 4 countries) than in your new data. A line of code specifically causes t... | error in getting predictions from a lme object
Thanks for providing the data so that I could perform some diagnostics.
Actually, this is an epic bug of predict.lme. Your factors have more levels in your initial data (for example you have more than |
36,122 | How to specify pulses/level-shifts in data when creating ARIMA in R? | If you want to empirically identify Pulses, Seasonal Pulses, Level(Step) Shifts and/or Local Time Trends you might want to look at How do I detect shifts in sales mix? or Detect changes in time series or Outlier detection for generic time series . Some commercial packages offer Intervention Analysis which does not incl... | How to specify pulses/level-shifts in data when creating ARIMA in R? | If you want to empirically identify Pulses, Seasonal Pulses, Level(Step) Shifts and/or Local Time Trends you might want to look at How do I detect shifts in sales mix? or Detect changes in time series | How to specify pulses/level-shifts in data when creating ARIMA in R?
If you want to empirically identify Pulses, Seasonal Pulses, Level(Step) Shifts and/or Local Time Trends you might want to look at How do I detect shifts in sales mix? or Detect changes in time series or Outlier detection for generic time series . Som... | How to specify pulses/level-shifts in data when creating ARIMA in R?
If you want to empirically identify Pulses, Seasonal Pulses, Level(Step) Shifts and/or Local Time Trends you might want to look at How do I detect shifts in sales mix? or Detect changes in time series |
36,123 | Methods of measuring strength of arbitrary non-linear relationships between two variables? | Plain old linear regression has a nice non-parametric interpretation as the average linear trend across all pairs of observations; see Berman 1988, "A theorem of Jacobi and its generalization". So, the data doesn't have to look linear in order to use it; any (broadly) monotonic trend could be summarized this way.
You ... | Methods of measuring strength of arbitrary non-linear relationships between two variables? | Plain old linear regression has a nice non-parametric interpretation as the average linear trend across all pairs of observations; see Berman 1988, "A theorem of Jacobi and its generalization". So, th | Methods of measuring strength of arbitrary non-linear relationships between two variables?
Plain old linear regression has a nice non-parametric interpretation as the average linear trend across all pairs of observations; see Berman 1988, "A theorem of Jacobi and its generalization". So, the data doesn't have to look l... | Methods of measuring strength of arbitrary non-linear relationships between two variables?
Plain old linear regression has a nice non-parametric interpretation as the average linear trend across all pairs of observations; see Berman 1988, "A theorem of Jacobi and its generalization". So, th |
36,124 | Methods of measuring strength of arbitrary non-linear relationships between two variables? | The "Amount of relationship" between two discrete variables $X$, $Y$ is formally measured by mutual information : $I(X,Y)$. While the covariance/correlation is somehow the amount of linear relationship, mutual information is somehow the amount of (any kind of) relationship. I'm pasting the picture form Wikipedia's page... | Methods of measuring strength of arbitrary non-linear relationships between two variables? | The "Amount of relationship" between two discrete variables $X$, $Y$ is formally measured by mutual information : $I(X,Y)$. While the covariance/correlation is somehow the amount of linear relationshi | Methods of measuring strength of arbitrary non-linear relationships between two variables?
The "Amount of relationship" between two discrete variables $X$, $Y$ is formally measured by mutual information : $I(X,Y)$. While the covariance/correlation is somehow the amount of linear relationship, mutual information is some... | Methods of measuring strength of arbitrary non-linear relationships between two variables?
The "Amount of relationship" between two discrete variables $X$, $Y$ is formally measured by mutual information : $I(X,Y)$. While the covariance/correlation is somehow the amount of linear relationshi |
36,125 | Methods of measuring strength of arbitrary non-linear relationships between two variables? | Eventually the most general form of an injective function is
$f(x) = y$
and you can use a discretized version of that function as a model for your data.
Then the problem reduces to determining the expected $y$ for separate regions $a<x<b$.
The method is not powerful because of the high amount of degrees of freedom ... | Methods of measuring strength of arbitrary non-linear relationships between two variables? | Eventually the most general form of an injective function is
$f(x) = y$
and you can use a discretized version of that function as a model for your data.
Then the problem reduces to determining the | Methods of measuring strength of arbitrary non-linear relationships between two variables?
Eventually the most general form of an injective function is
$f(x) = y$
and you can use a discretized version of that function as a model for your data.
Then the problem reduces to determining the expected $y$ for separate reg... | Methods of measuring strength of arbitrary non-linear relationships between two variables?
Eventually the most general form of an injective function is
$f(x) = y$
and you can use a discretized version of that function as a model for your data.
Then the problem reduces to determining the |
36,126 | Methods of measuring strength of arbitrary non-linear relationships between two variables? | Needs to be a method that is quick to calculate, similar to correlation, but can detect quadratic relationships for example.
The Spearman correlation, which was mentioned in another answer, fits the bill. It is calculated by simply converting the data to ranks and then finding the Pearson correlation for the ranks. It... | Methods of measuring strength of arbitrary non-linear relationships between two variables? | Needs to be a method that is quick to calculate, similar to correlation, but can detect quadratic relationships for example.
The Spearman correlation, which was mentioned in another answer, fits the | Methods of measuring strength of arbitrary non-linear relationships between two variables?
Needs to be a method that is quick to calculate, similar to correlation, but can detect quadratic relationships for example.
The Spearman correlation, which was mentioned in another answer, fits the bill. It is calculated by sim... | Methods of measuring strength of arbitrary non-linear relationships between two variables?
Needs to be a method that is quick to calculate, similar to correlation, but can detect quadratic relationships for example.
The Spearman correlation, which was mentioned in another answer, fits the |
36,127 | Methods of measuring strength of arbitrary non-linear relationships between two variables? | It's not totally clear to be what you are trying to measure, but I'll try to give you info that might help. There are correlation measures like Cronback's Alpha that can be used to assess the internal consistency/ relationship among a set of variables. You could also use things like general additive models (GAMs) to te... | Methods of measuring strength of arbitrary non-linear relationships between two variables? | It's not totally clear to be what you are trying to measure, but I'll try to give you info that might help. There are correlation measures like Cronback's Alpha that can be used to assess the internal | Methods of measuring strength of arbitrary non-linear relationships between two variables?
It's not totally clear to be what you are trying to measure, but I'll try to give you info that might help. There are correlation measures like Cronback's Alpha that can be used to assess the internal consistency/ relationship am... | Methods of measuring strength of arbitrary non-linear relationships between two variables?
It's not totally clear to be what you are trying to measure, but I'll try to give you info that might help. There are correlation measures like Cronback's Alpha that can be used to assess the internal |
36,128 | Methods of measuring strength of arbitrary non-linear relationships between two variables? | You may try maximal information coefficient. It outperforms selected methods in the paper and works well in detecting nonlinear relationships between two random variables. | Methods of measuring strength of arbitrary non-linear relationships between two variables? | You may try maximal information coefficient. It outperforms selected methods in the paper and works well in detecting nonlinear relationships between two random variables. | Methods of measuring strength of arbitrary non-linear relationships between two variables?
You may try maximal information coefficient. It outperforms selected methods in the paper and works well in detecting nonlinear relationships between two random variables. | Methods of measuring strength of arbitrary non-linear relationships between two variables?
You may try maximal information coefficient. It outperforms selected methods in the paper and works well in detecting nonlinear relationships between two random variables. |
36,129 | Methods of measuring strength of arbitrary non-linear relationships between two variables? | I cannot comment thus I have to post the answer. Have a look at Dynamic Time Warping, simple algorithm that can kind of detect/compare patterns between two time series, which can have even different granularity.
https://en.wikipedia.org/wiki/Dynamic_time_warping | Methods of measuring strength of arbitrary non-linear relationships between two variables? | I cannot comment thus I have to post the answer. Have a look at Dynamic Time Warping, simple algorithm that can kind of detect/compare patterns between two time series, which can have even different g | Methods of measuring strength of arbitrary non-linear relationships between two variables?
I cannot comment thus I have to post the answer. Have a look at Dynamic Time Warping, simple algorithm that can kind of detect/compare patterns between two time series, which can have even different granularity.
https://en.wikipe... | Methods of measuring strength of arbitrary non-linear relationships between two variables?
I cannot comment thus I have to post the answer. Have a look at Dynamic Time Warping, simple algorithm that can kind of detect/compare patterns between two time series, which can have even different g |
36,130 | Test of multicollinearity among independent variables in logistic regression | You can use whatever method you would use for ordinary regression. The dependent variable is irrelevant to multicollinearity issues, so it doesn't matter if you used logistic regression or regular regression or whatever. | Test of multicollinearity among independent variables in logistic regression | You can use whatever method you would use for ordinary regression. The dependent variable is irrelevant to multicollinearity issues, so it doesn't matter if you used logistic regression or regular reg | Test of multicollinearity among independent variables in logistic regression
You can use whatever method you would use for ordinary regression. The dependent variable is irrelevant to multicollinearity issues, so it doesn't matter if you used logistic regression or regular regression or whatever. | Test of multicollinearity among independent variables in logistic regression
You can use whatever method you would use for ordinary regression. The dependent variable is irrelevant to multicollinearity issues, so it doesn't matter if you used logistic regression or regular reg |
36,131 | Test of multicollinearity among independent variables in logistic regression | You can take the reference of condition index as well. a value greater than 30 indicates there is a near dependency in most cases. you can then go by either the correlation matrix or durbin watson test. | Test of multicollinearity among independent variables in logistic regression | You can take the reference of condition index as well. a value greater than 30 indicates there is a near dependency in most cases. you can then go by either the correlation matrix or durbin watson tes | Test of multicollinearity among independent variables in logistic regression
You can take the reference of condition index as well. a value greater than 30 indicates there is a near dependency in most cases. you can then go by either the correlation matrix or durbin watson test. | Test of multicollinearity among independent variables in logistic regression
You can take the reference of condition index as well. a value greater than 30 indicates there is a near dependency in most cases. you can then go by either the correlation matrix or durbin watson tes |
36,132 | Test of multicollinearity among independent variables in logistic regression | You could construct a correlation matrix and look for high values. An alternative would indeed be the VIF values as already mentioned.
Both are quite arbitrary and rely on rules of thumb. For example what's the threshold for a correlation to be 'dangerous'?
You could try to use factor scores on the correlated variabl... | Test of multicollinearity among independent variables in logistic regression | You could construct a correlation matrix and look for high values. An alternative would indeed be the VIF values as already mentioned.
Both are quite arbitrary and rely on rules of thumb. For example | Test of multicollinearity among independent variables in logistic regression
You could construct a correlation matrix and look for high values. An alternative would indeed be the VIF values as already mentioned.
Both are quite arbitrary and rely on rules of thumb. For example what's the threshold for a correlation to ... | Test of multicollinearity among independent variables in logistic regression
You could construct a correlation matrix and look for high values. An alternative would indeed be the VIF values as already mentioned.
Both are quite arbitrary and rely on rules of thumb. For example |
36,133 | Test of multicollinearity among independent variables in logistic regression | Examining a correlation matrix is helpful, but it is not a sufficient check since variables may be correlated when taken together but not pairwise. I recommend examining tolerance or Variance Inflation Factor diagnostics in regression using a weighted regression where the weights are set to be equal to phat x (1-phat)... | Test of multicollinearity among independent variables in logistic regression | Examining a correlation matrix is helpful, but it is not a sufficient check since variables may be correlated when taken together but not pairwise. I recommend examining tolerance or Variance Inflati | Test of multicollinearity among independent variables in logistic regression
Examining a correlation matrix is helpful, but it is not a sufficient check since variables may be correlated when taken together but not pairwise. I recommend examining tolerance or Variance Inflation Factor diagnostics in regression using a... | Test of multicollinearity among independent variables in logistic regression
Examining a correlation matrix is helpful, but it is not a sufficient check since variables may be correlated when taken together but not pairwise. I recommend examining tolerance or Variance Inflati |
36,134 | What are good references on calculating confidence intervals using subsampling or the delete-d jackknife? | The practice in the field seems to be to rely on asymptotic normality of the estimate and to use the jackknife just as an estimate of variance to help calculate standard errors, which are then plugged into a hoped-for normal distribution of the estimate.
If there's reason to think the estimate doesn't have a normal dis... | What are good references on calculating confidence intervals using subsampling or the delete-d jackk | The practice in the field seems to be to rely on asymptotic normality of the estimate and to use the jackknife just as an estimate of variance to help calculate standard errors, which are then plugged | What are good references on calculating confidence intervals using subsampling or the delete-d jackknife?
The practice in the field seems to be to rely on asymptotic normality of the estimate and to use the jackknife just as an estimate of variance to help calculate standard errors, which are then plugged into a hoped-... | What are good references on calculating confidence intervals using subsampling or the delete-d jackk
The practice in the field seems to be to rely on asymptotic normality of the estimate and to use the jackknife just as an estimate of variance to help calculate standard errors, which are then plugged |
36,135 | What are good references on calculating confidence intervals using subsampling or the delete-d jackknife? | For subsampling you can look at material in my book or Efron and Tibshirani. But the best reference that has been over looked so far is the book by Politis, Romano and Wolf. The original work on this predates the bootstrap and is due mainly to Hartigan in his 1969 paper in JASA. Here are links to the books.
Subsampl... | What are good references on calculating confidence intervals using subsampling or the delete-d jackk | For subsampling you can look at material in my book or Efron and Tibshirani. But the best reference that has been over looked so far is the book by Politis, Romano and Wolf. The original work on thi | What are good references on calculating confidence intervals using subsampling or the delete-d jackknife?
For subsampling you can look at material in my book or Efron and Tibshirani. But the best reference that has been over looked so far is the book by Politis, Romano and Wolf. The original work on this predates the... | What are good references on calculating confidence intervals using subsampling or the delete-d jackk
For subsampling you can look at material in my book or Efron and Tibshirani. But the best reference that has been over looked so far is the book by Politis, Romano and Wolf. The original work on thi |
36,136 | Fitting the parameters of a stable distribution | As suggested in the comments,
you can use fitdistr, with the density function from fBasics.
# Sample data
x <- rt(100,df=4)
# Density (I reparametrize it to remove the constraints
# on the parameters)
library(fBasics)
library(stabledist)
f <- function(u,a,b,c,d) {
cat(a,b,c,d,"\n") # Some logging (it is very slow... | Fitting the parameters of a stable distribution | As suggested in the comments,
you can use fitdistr, with the density function from fBasics.
# Sample data
x <- rt(100,df=4)
# Density (I reparametrize it to remove the constraints
# on the paramete | Fitting the parameters of a stable distribution
As suggested in the comments,
you can use fitdistr, with the density function from fBasics.
# Sample data
x <- rt(100,df=4)
# Density (I reparametrize it to remove the constraints
# on the parameters)
library(fBasics)
library(stabledist)
f <- function(u,a,b,c,d) {
ca... | Fitting the parameters of a stable distribution
As suggested in the comments,
you can use fitdistr, with the density function from fBasics.
# Sample data
x <- rt(100,df=4)
# Density (I reparametrize it to remove the constraints
# on the paramete |
36,137 | Fitting the parameters of a stable distribution | @Vincent's answer sounds good, but here is another approach: Since you know the Fourier transform of the distribution, take the appropriate Fourier transformation of the data, and find parameters that give the best fit in Fourier space.
I think this method should work just as well in theory, and in practice would avo... | Fitting the parameters of a stable distribution | @Vincent's answer sounds good, but here is another approach: Since you know the Fourier transform of the distribution, take the appropriate Fourier transformation of the data, and find parameters that | Fitting the parameters of a stable distribution
@Vincent's answer sounds good, but here is another approach: Since you know the Fourier transform of the distribution, take the appropriate Fourier transformation of the data, and find parameters that give the best fit in Fourier space.
I think this method should work j... | Fitting the parameters of a stable distribution
@Vincent's answer sounds good, but here is another approach: Since you know the Fourier transform of the distribution, take the appropriate Fourier transformation of the data, and find parameters that |
36,138 | Fitting the parameters of a stable distribution | One way to fit the $\alpha$ parameter is via the Nagaev transform described by Okoneshnikov.
An alternative is the 'Probability of Return' method of Mantegna and Stanley, which is considerably easier.
edit: the other 'classical' method is of Kogon & Williams (S.M. Kogon, Douglas B. Williams, "On Characteristic Function... | Fitting the parameters of a stable distribution | One way to fit the $\alpha$ parameter is via the Nagaev transform described by Okoneshnikov.
An alternative is the 'Probability of Return' method of Mantegna and Stanley, which is considerably easier. | Fitting the parameters of a stable distribution
One way to fit the $\alpha$ parameter is via the Nagaev transform described by Okoneshnikov.
An alternative is the 'Probability of Return' method of Mantegna and Stanley, which is considerably easier.
edit: the other 'classical' method is of Kogon & Williams (S.M. Kogon, ... | Fitting the parameters of a stable distribution
One way to fit the $\alpha$ parameter is via the Nagaev transform described by Okoneshnikov.
An alternative is the 'Probability of Return' method of Mantegna and Stanley, which is considerably easier. |
36,139 | Introduction to Kalman filters | The most human readable intro with examples I have found so far is the SIGGRAPH Course Pack. | Introduction to Kalman filters | The most human readable intro with examples I have found so far is the SIGGRAPH Course Pack. | Introduction to Kalman filters
The most human readable intro with examples I have found so far is the SIGGRAPH Course Pack. | Introduction to Kalman filters
The most human readable intro with examples I have found so far is the SIGGRAPH Course Pack. |
36,140 | Introduction to Kalman filters | [Reposting a comment by @Vincent-Zoonekynd from Estimate in presence of missing observations]: Here is a very simple introduction to the Kalman filter, to estimate the position of a robot (think of the position as the parameter you are trying to estimate): sites.google.com/site/udacitymirrorcs373/cs-373/unit-2 (you may... | Introduction to Kalman filters | [Reposting a comment by @Vincent-Zoonekynd from Estimate in presence of missing observations]: Here is a very simple introduction to the Kalman filter, to estimate the position of a robot (think of th | Introduction to Kalman filters
[Reposting a comment by @Vincent-Zoonekynd from Estimate in presence of missing observations]: Here is a very simple introduction to the Kalman filter, to estimate the position of a robot (think of the position as the parameter you are trying to estimate): sites.google.com/site/udacitymir... | Introduction to Kalman filters
[Reposting a comment by @Vincent-Zoonekynd from Estimate in presence of missing observations]: Here is a very simple introduction to the Kalman filter, to estimate the position of a robot (think of th |
36,141 | Introduction to Kalman filters | Advanced Kalman Filtering, Least-Squares and Modeling: A Practical Handbook by Bruce Gibbs is liberally sprinkled with examples.
One book I'm not fond of is A Kalman Filter Primer. | Introduction to Kalman filters | Advanced Kalman Filtering, Least-Squares and Modeling: A Practical Handbook by Bruce Gibbs is liberally sprinkled with examples.
One book I'm not fond of is A Kalman Filter Primer. | Introduction to Kalman filters
Advanced Kalman Filtering, Least-Squares and Modeling: A Practical Handbook by Bruce Gibbs is liberally sprinkled with examples.
One book I'm not fond of is A Kalman Filter Primer. | Introduction to Kalman filters
Advanced Kalman Filtering, Least-Squares and Modeling: A Practical Handbook by Bruce Gibbs is liberally sprinkled with examples.
One book I'm not fond of is A Kalman Filter Primer. |
36,142 | Introduction to Kalman filters | I did get Kalman cleared after reading 'Kalman Filter for Beginners with Matlab examples' by Phil Kim http://books.google.co.uk/books?id=W8u_XwAACAAJ&dq=kalman+filter+phil+kim&source=bl&ots=N-I0YhBX_U&sig=pcfeeEGHYmYDr7bockF5kSIMM_s&hl=en&sa=X&ei=ir5xUM3gM8Op0QWI8YDwDQ&ved=0CC4Q6AEwAA
The book starts with some basic id... | Introduction to Kalman filters | I did get Kalman cleared after reading 'Kalman Filter for Beginners with Matlab examples' by Phil Kim http://books.google.co.uk/books?id=W8u_XwAACAAJ&dq=kalman+filter+phil+kim&source=bl&ots=N-I0YhBX_U | Introduction to Kalman filters
I did get Kalman cleared after reading 'Kalman Filter for Beginners with Matlab examples' by Phil Kim http://books.google.co.uk/books?id=W8u_XwAACAAJ&dq=kalman+filter+phil+kim&source=bl&ots=N-I0YhBX_U&sig=pcfeeEGHYmYDr7bockF5kSIMM_s&hl=en&sa=X&ei=ir5xUM3gM8Op0QWI8YDwDQ&ved=0CC4Q6AEwAA
The... | Introduction to Kalman filters
I did get Kalman cleared after reading 'Kalman Filter for Beginners with Matlab examples' by Phil Kim http://books.google.co.uk/books?id=W8u_XwAACAAJ&dq=kalman+filter+phil+kim&source=bl&ots=N-I0YhBX_U |
36,143 | Introduction to Kalman filters | The book An Introduction To State Space Time Series Analysis, by Commandeur and Koopman, is small and fairly readable. It uses Ssfpack and STAMP to implement things, which made it harder for me to transfer the knowledge. | Introduction to Kalman filters | The book An Introduction To State Space Time Series Analysis, by Commandeur and Koopman, is small and fairly readable. It uses Ssfpack and STAMP to implement things, which made it harder for me to tra | Introduction to Kalman filters
The book An Introduction To State Space Time Series Analysis, by Commandeur and Koopman, is small and fairly readable. It uses Ssfpack and STAMP to implement things, which made it harder for me to transfer the knowledge. | Introduction to Kalman filters
The book An Introduction To State Space Time Series Analysis, by Commandeur and Koopman, is small and fairly readable. It uses Ssfpack and STAMP to implement things, which made it harder for me to tra |
36,144 | To use Discrete Fourier Transform to invert a covariance matrix | A circulant is a matrix whose first column is a vector $x$ and its subsequent columns are obtained by rotation of one element to the right. Here is R code to produce any circulant from its first column x:
rotate <- function(x,k) {c(tail(x,-k), head(x,k))}
circulant <- function(x) {
n=length(x)
apply(matrix(0:(... | To use Discrete Fourier Transform to invert a covariance matrix | A circulant is a matrix whose first column is a vector $x$ and its subsequent columns are obtained by rotation of one element to the right. Here is R code to produce any circulant from its first colu | To use Discrete Fourier Transform to invert a covariance matrix
A circulant is a matrix whose first column is a vector $x$ and its subsequent columns are obtained by rotation of one element to the right. Here is R code to produce any circulant from its first column x:
rotate <- function(x,k) {c(tail(x,-k), head(x,k))}... | To use Discrete Fourier Transform to invert a covariance matrix
A circulant is a matrix whose first column is a vector $x$ and its subsequent columns are obtained by rotation of one element to the right. Here is R code to produce any circulant from its first colu |
36,145 | To use Discrete Fourier Transform to invert a covariance matrix | Have you tried a correction- by adding a small $\epsilon$ perturbation to the diagonal of the matrix you are trying to invert. This is a standard processing routine used to defer the singularity issue while inverting a covariance matrix or a hessian. | To use Discrete Fourier Transform to invert a covariance matrix | Have you tried a correction- by adding a small $\epsilon$ perturbation to the diagonal of the matrix you are trying to invert. This is a standard processing routine used to defer the singularity issue | To use Discrete Fourier Transform to invert a covariance matrix
Have you tried a correction- by adding a small $\epsilon$ perturbation to the diagonal of the matrix you are trying to invert. This is a standard processing routine used to defer the singularity issue while inverting a covariance matrix or a hessian. | To use Discrete Fourier Transform to invert a covariance matrix
Have you tried a correction- by adding a small $\epsilon$ perturbation to the diagonal of the matrix you are trying to invert. This is a standard processing routine used to defer the singularity issue |
36,146 | To use Discrete Fourier Transform to invert a covariance matrix | Is the Wikipedia article on circulant matrices clear? This is something that in a time series context is discussed, for instance, in the first pages of Hannan's Time Series book. The eigenvalues of a circulant matrix are given by the Fourier transform of what (again in a time series context) would be the autocovariance... | To use Discrete Fourier Transform to invert a covariance matrix | Is the Wikipedia article on circulant matrices clear? This is something that in a time series context is discussed, for instance, in the first pages of Hannan's Time Series book. The eigenvalues of a | To use Discrete Fourier Transform to invert a covariance matrix
Is the Wikipedia article on circulant matrices clear? This is something that in a time series context is discussed, for instance, in the first pages of Hannan's Time Series book. The eigenvalues of a circulant matrix are given by the Fourier transform of w... | To use Discrete Fourier Transform to invert a covariance matrix
Is the Wikipedia article on circulant matrices clear? This is something that in a time series context is discussed, for instance, in the first pages of Hannan's Time Series book. The eigenvalues of a |
36,147 | Multilayer neural networks for multivariate temporal data | I also agree with Ran, most of the deep learning techniques are tested with image data set. Please checkout this research paper, it talks about audio classification using deep learning techniques. | Multilayer neural networks for multivariate temporal data | I also agree with Ran, most of the deep learning techniques are tested with image data set. Please checkout this research paper, it talks about audio classification using deep learning techniques. | Multilayer neural networks for multivariate temporal data
I also agree with Ran, most of the deep learning techniques are tested with image data set. Please checkout this research paper, it talks about audio classification using deep learning techniques. | Multilayer neural networks for multivariate temporal data
I also agree with Ran, most of the deep learning techniques are tested with image data set. Please checkout this research paper, it talks about audio classification using deep learning techniques. |
36,148 | Multilayer neural networks for multivariate temporal data | There are multiple papers by Hinton et al. which deal with temporal data and also audio (http://www.cs.toronto.edu/~hinton/papers.html). For example:
Acoustic Modeling using Deep Belief Networks, 2012.
Learning a better Representation of Speech Sound Waves using
Restricted Boltzmann Machines, 2011.
Deep Belief Network... | Multilayer neural networks for multivariate temporal data | There are multiple papers by Hinton et al. which deal with temporal data and also audio (http://www.cs.toronto.edu/~hinton/papers.html). For example:
Acoustic Modeling using Deep Belief Networks, 201 | Multilayer neural networks for multivariate temporal data
There are multiple papers by Hinton et al. which deal with temporal data and also audio (http://www.cs.toronto.edu/~hinton/papers.html). For example:
Acoustic Modeling using Deep Belief Networks, 2012.
Learning a better Representation of Speech Sound Waves usin... | Multilayer neural networks for multivariate temporal data
There are multiple papers by Hinton et al. which deal with temporal data and also audio (http://www.cs.toronto.edu/~hinton/papers.html). For example:
Acoustic Modeling using Deep Belief Networks, 201 |
36,149 | Multilayer neural networks for multivariate temporal data | I can't post comments yet, but I have the following remark.
With multivariate data, I usually think of different "types" of data, e.g. a mixture of bool, multiselection, or floating point data (here it is called mixed-variate). As I see it, the input of different audio signals is therefore not multivariate, but multidi... | Multilayer neural networks for multivariate temporal data | I can't post comments yet, but I have the following remark.
With multivariate data, I usually think of different "types" of data, e.g. a mixture of bool, multiselection, or floating point data (here i | Multilayer neural networks for multivariate temporal data
I can't post comments yet, but I have the following remark.
With multivariate data, I usually think of different "types" of data, e.g. a mixture of bool, multiselection, or floating point data (here it is called mixed-variate). As I see it, the input of differen... | Multilayer neural networks for multivariate temporal data
I can't post comments yet, but I have the following remark.
With multivariate data, I usually think of different "types" of data, e.g. a mixture of bool, multiselection, or floating point data (here i |
36,150 | Bootstrapping stratified sample that is weighted to population - reweighting during the bootstrap? | In R i would tell you to see if the functions related with "bootweights" in the survey package suit you in any way. But since you have already gone over that package I don't think you will find many alternatives ... I also looked for a similiar thing a couple of weeks ago and ended up implementing my own code.
For the ... | Bootstrapping stratified sample that is weighted to population - reweighting during the bootstrap? | In R i would tell you to see if the functions related with "bootweights" in the survey package suit you in any way. But since you have already gone over that package I don't think you will find many a | Bootstrapping stratified sample that is weighted to population - reweighting during the bootstrap?
In R i would tell you to see if the functions related with "bootweights" in the survey package suit you in any way. But since you have already gone over that package I don't think you will find many alternatives ... I als... | Bootstrapping stratified sample that is weighted to population - reweighting during the bootstrap?
In R i would tell you to see if the functions related with "bootweights" in the survey package suit you in any way. But since you have already gone over that package I don't think you will find many a |
36,151 | Bootstrapping stratified sample that is weighted to population - reweighting during the bootstrap? | References on the reweighting method
When you reweight your data to match known population totals (using raking, post-stratification, or some other form of calibration), it has long been common practice to repeat the reweighting procedure for each replicate sample. This practice and its justification are described clea... | Bootstrapping stratified sample that is weighted to population - reweighting during the bootstrap? | References on the reweighting method
When you reweight your data to match known population totals (using raking, post-stratification, or some other form of calibration), it has long been common practi | Bootstrapping stratified sample that is weighted to population - reweighting during the bootstrap?
References on the reweighting method
When you reweight your data to match known population totals (using raking, post-stratification, or some other form of calibration), it has long been common practice to repeat the rewe... | Bootstrapping stratified sample that is weighted to population - reweighting during the bootstrap?
References on the reweighting method
When you reweight your data to match known population totals (using raking, post-stratification, or some other form of calibration), it has long been common practi |
36,152 | How to specify Bayesian mixed effects model in BUGS | You are (were) almost there. Just a few comments - you don't have to make the prior for the beta[,1:2] parameters a joint MV normal; you can make the prior such that beta[i,1] and beta[i,2] are independent, which simplifies things (for example, no prior covariance need be specified.) Note that doing so doesn't mean t... | How to specify Bayesian mixed effects model in BUGS | You are (were) almost there. Just a few comments - you don't have to make the prior for the beta[,1:2] parameters a joint MV normal; you can make the prior such that beta[i,1] and beta[i,2] are indep | How to specify Bayesian mixed effects model in BUGS
You are (were) almost there. Just a few comments - you don't have to make the prior for the beta[,1:2] parameters a joint MV normal; you can make the prior such that beta[i,1] and beta[i,2] are independent, which simplifies things (for example, no prior covariance ne... | How to specify Bayesian mixed effects model in BUGS
You are (were) almost there. Just a few comments - you don't have to make the prior for the beta[,1:2] parameters a joint MV normal; you can make the prior such that beta[i,1] and beta[i,2] are indep |
36,153 | Setting the threshold p-value as part of hypothesis generation | If you intend to use the Neyman-Pearson approach then you definitely cannot set the cutoff for significance after the data has been analysed. However, that is not the only approach to statistical inference, and in many cases it is not the best approach. N-P is certainly not well matched to a task that you specify as hy... | Setting the threshold p-value as part of hypothesis generation | If you intend to use the Neyman-Pearson approach then you definitely cannot set the cutoff for significance after the data has been analysed. However, that is not the only approach to statistical infe | Setting the threshold p-value as part of hypothesis generation
If you intend to use the Neyman-Pearson approach then you definitely cannot set the cutoff for significance after the data has been analysed. However, that is not the only approach to statistical inference, and in many cases it is not the best approach. N-P... | Setting the threshold p-value as part of hypothesis generation
If you intend to use the Neyman-Pearson approach then you definitely cannot set the cutoff for significance after the data has been analysed. However, that is not the only approach to statistical infe |
36,154 | Setting the threshold p-value as part of hypothesis generation | I suggest you try a different approach -- False Discovery Rate (FDR).
The FDR for any given P value cutoff is the expected fraction of those comparisons (with P less than your cutoff) where the null hypothesis is actually false (while 1.0 - FDR is the fraction of the comparisons where you expect the null hypothesis to... | Setting the threshold p-value as part of hypothesis generation | I suggest you try a different approach -- False Discovery Rate (FDR).
The FDR for any given P value cutoff is the expected fraction of those comparisons (with P less than your cutoff) where the null | Setting the threshold p-value as part of hypothesis generation
I suggest you try a different approach -- False Discovery Rate (FDR).
The FDR for any given P value cutoff is the expected fraction of those comparisons (with P less than your cutoff) where the null hypothesis is actually false (while 1.0 - FDR is the frac... | Setting the threshold p-value as part of hypothesis generation
I suggest you try a different approach -- False Discovery Rate (FDR).
The FDR for any given P value cutoff is the expected fraction of those comparisons (with P less than your cutoff) where the null |
36,155 | Setting the threshold p-value as part of hypothesis generation | I think you're really wondering about $p$-value correction. Bonferroni's is the simplest. You should use one if you have multiple post-hoc tests. This is what you were discussing, except that people typically consider this an adjustment to the $p$-value rather than an adjustment to $\alpha$.
Also, since your sample siz... | Setting the threshold p-value as part of hypothesis generation | I think you're really wondering about $p$-value correction. Bonferroni's is the simplest. You should use one if you have multiple post-hoc tests. This is what you were discussing, except that people t | Setting the threshold p-value as part of hypothesis generation
I think you're really wondering about $p$-value correction. Bonferroni's is the simplest. You should use one if you have multiple post-hoc tests. This is what you were discussing, except that people typically consider this an adjustment to the $p$-value rat... | Setting the threshold p-value as part of hypothesis generation
I think you're really wondering about $p$-value correction. Bonferroni's is the simplest. You should use one if you have multiple post-hoc tests. This is what you were discussing, except that people t |
36,156 | Estimating the intersection of two lines | One straightforward way is to obtain the maximum likelihood estimator of $x^{*}$ directly. Using the first subscript to designate the line ($1$ or $2$), the model is
$$y_{ij} = m_i(x_{ij} - x^{*}) + y^{*} + \varepsilon_{ij},$$
$1 \le j \le n_i$, $\varepsilon_{ij} \sim \text{Normal}(0, \sigma_i / \omega_{ij})$ and indep... | Estimating the intersection of two lines | One straightforward way is to obtain the maximum likelihood estimator of $x^{*}$ directly. Using the first subscript to designate the line ($1$ or $2$), the model is
$$y_{ij} = m_i(x_{ij} - x^{*}) + y | Estimating the intersection of two lines
One straightforward way is to obtain the maximum likelihood estimator of $x^{*}$ directly. Using the first subscript to designate the line ($1$ or $2$), the model is
$$y_{ij} = m_i(x_{ij} - x^{*}) + y^{*} + \varepsilon_{ij},$$
$1 \le j \le n_i$, $\varepsilon_{ij} \sim \text{Norm... | Estimating the intersection of two lines
One straightforward way is to obtain the maximum likelihood estimator of $x^{*}$ directly. Using the first subscript to designate the line ($1$ or $2$), the model is
$$y_{ij} = m_i(x_{ij} - x^{*}) + y |
36,157 | Why does t statistic increase with the sample size? | A little change in notation might help in answering your question: What you call $\mu$ is often called $\mu_{0}$ because it is the population mean under the null hypothesis, whereas $\mu$ is the actual population mean - which is unknown because we don't know whether the null hypothesis is true. Also, what you call $\si... | Why does t statistic increase with the sample size? | A little change in notation might help in answering your question: What you call $\mu$ is often called $\mu_{0}$ because it is the population mean under the null hypothesis, whereas $\mu$ is the actua | Why does t statistic increase with the sample size?
A little change in notation might help in answering your question: What you call $\mu$ is often called $\mu_{0}$ because it is the population mean under the null hypothesis, whereas $\mu$ is the actual population mean - which is unknown because we don't know whether t... | Why does t statistic increase with the sample size?
A little change in notation might help in answering your question: What you call $\mu$ is often called $\mu_{0}$ because it is the population mean under the null hypothesis, whereas $\mu$ is the actua |
36,158 | Why does t statistic increase with the sample size? | Someone else can probably give a more rigorous answer, but:
For any given (fixed) difference between $\bar{X}$ and $\mu$, the difference is more meaningful if n is high.
Increasing n will result in a sample mean closer to the population mean, but only in the case that your sample is not different from the population. ... | Why does t statistic increase with the sample size? | Someone else can probably give a more rigorous answer, but:
For any given (fixed) difference between $\bar{X}$ and $\mu$, the difference is more meaningful if n is high.
Increasing n will result in a | Why does t statistic increase with the sample size?
Someone else can probably give a more rigorous answer, but:
For any given (fixed) difference between $\bar{X}$ and $\mu$, the difference is more meaningful if n is high.
Increasing n will result in a sample mean closer to the population mean, but only in the case that... | Why does t statistic increase with the sample size?
Someone else can probably give a more rigorous answer, but:
For any given (fixed) difference between $\bar{X}$ and $\mu$, the difference is more meaningful if n is high.
Increasing n will result in a |
36,159 | Why does t statistic increase with the sample size? | Your last sentence seems to capsulize the confusion.
You wrote
But this formula seems counter-intuitive to me as bigger sample size
(higher n) should give sample mean closer to population mean.
but this is true only if the sample is from a population that has the same mean as the population it is being compared to... | Why does t statistic increase with the sample size? | Your last sentence seems to capsulize the confusion.
You wrote
But this formula seems counter-intuitive to me as bigger sample size
(higher n) should give sample mean closer to population mean.
b | Why does t statistic increase with the sample size?
Your last sentence seems to capsulize the confusion.
You wrote
But this formula seems counter-intuitive to me as bigger sample size
(higher n) should give sample mean closer to population mean.
but this is true only if the sample is from a population that has the... | Why does t statistic increase with the sample size?
Your last sentence seems to capsulize the confusion.
You wrote
But this formula seems counter-intuitive to me as bigger sample size
(higher n) should give sample mean closer to population mean.
b |
36,160 | What is the expected value of the sample variance under a linear regression with omitted variables of an AR(2) process? | Nice introductory book on the topic related to different aspects of time series models could be Introduction to Time Series and Forecasting by Brockwell and Davis among many others. Roughly speaking, the characteristic of the autoregressive process of order $p$ is linked to the partial autocorrelation function. Estimat... | What is the expected value of the sample variance under a linear regression with omitted variables o | Nice introductory book on the topic related to different aspects of time series models could be Introduction to Time Series and Forecasting by Brockwell and Davis among many others. Roughly speaking, | What is the expected value of the sample variance under a linear regression with omitted variables of an AR(2) process?
Nice introductory book on the topic related to different aspects of time series models could be Introduction to Time Series and Forecasting by Brockwell and Davis among many others. Roughly speaking, ... | What is the expected value of the sample variance under a linear regression with omitted variables o
Nice introductory book on the topic related to different aspects of time series models could be Introduction to Time Series and Forecasting by Brockwell and Davis among many others. Roughly speaking, |
36,161 | Machine learning techniques for time series estimation - forecasting price | An ARMAX model might be a good place to start. | Machine learning techniques for time series estimation - forecasting price | An ARMAX model might be a good place to start. | Machine learning techniques for time series estimation - forecasting price
An ARMAX model might be a good place to start. | Machine learning techniques for time series estimation - forecasting price
An ARMAX model might be a good place to start. |
36,162 | Machine learning techniques for time series estimation - forecasting price | Recurrent neural networks:
no assumptions on the distributions of $f_i$,
distribution of $x_t$ can be modelled via an adequate loss function (sum of squares for Gaussian, sum of differences for Laplace, cross entropy, kulback leibler divergences, ...)
rather difficult to implement (need advanced techniques such as Hes... | Machine learning techniques for time series estimation - forecasting price | Recurrent neural networks:
no assumptions on the distributions of $f_i$,
distribution of $x_t$ can be modelled via an adequate loss function (sum of squares for Gaussian, sum of differences for Lapla | Machine learning techniques for time series estimation - forecasting price
Recurrent neural networks:
no assumptions on the distributions of $f_i$,
distribution of $x_t$ can be modelled via an adequate loss function (sum of squares for Gaussian, sum of differences for Laplace, cross entropy, kulback leibler divergence... | Machine learning techniques for time series estimation - forecasting price
Recurrent neural networks:
no assumptions on the distributions of $f_i$,
distribution of $x_t$ can be modelled via an adequate loss function (sum of squares for Gaussian, sum of differences for Lapla |
36,163 | Machine learning techniques for time series estimation - forecasting price | Critically, what's your data frequency? Is this high or low frequency news? Also critically, you want to forecast returns, not prices.
Your question is sufficiently broad that every supervised learning/regression technique can be listed legitimately.
For example, your news could be high frequency news, meaning the res... | Machine learning techniques for time series estimation - forecasting price | Critically, what's your data frequency? Is this high or low frequency news? Also critically, you want to forecast returns, not prices.
Your question is sufficiently broad that every supervised learnin | Machine learning techniques for time series estimation - forecasting price
Critically, what's your data frequency? Is this high or low frequency news? Also critically, you want to forecast returns, not prices.
Your question is sufficiently broad that every supervised learning/regression technique can be listed legitima... | Machine learning techniques for time series estimation - forecasting price
Critically, what's your data frequency? Is this high or low frequency news? Also critically, you want to forecast returns, not prices.
Your question is sufficiently broad that every supervised learnin |
36,164 | Interpretation of intercept term in poisson model with offset and covariates | I think that you want offset(log(population)) in your models above.
The offset is just a term included in the model without estimating a coefficient for it (fixing the coefficient at 1). Since the standard transformation in poisson regression is log, you can think of incuding the offset of log(population) as a rough... | Interpretation of intercept term in poisson model with offset and covariates | I think that you want offset(log(population)) in your models above.
The offset is just a term included in the model without estimating a coefficient for it (fixing the coefficient at 1). Since the | Interpretation of intercept term in poisson model with offset and covariates
I think that you want offset(log(population)) in your models above.
The offset is just a term included in the model without estimating a coefficient for it (fixing the coefficient at 1). Since the standard transformation in poisson regressi... | Interpretation of intercept term in poisson model with offset and covariates
I think that you want offset(log(population)) in your models above.
The offset is just a term included in the model without estimating a coefficient for it (fixing the coefficient at 1). Since the |
36,165 | Estimating effect of latent variable in regression | One answer is "no." Another is, "of course."
No
To simplify notation, let $\lambda(x) = 1/(1 + \exp(-x))$, the inverse logit. Because $\lambda(x) = 1 - \lambda(-x)$,
$$\beta_0 + \beta_1 \lambda(x) = (\beta_0 + \beta_1) - \beta_1 \lambda(-x)).$$
Therefore it is impossible to distinguish the parameters $(\beta_0, \beta... | Estimating effect of latent variable in regression | One answer is "no." Another is, "of course."
No
To simplify notation, let $\lambda(x) = 1/(1 + \exp(-x))$, the inverse logit. Because $\lambda(x) = 1 - \lambda(-x)$,
$$\beta_0 + \beta_1 \lambda(x) = | Estimating effect of latent variable in regression
One answer is "no." Another is, "of course."
No
To simplify notation, let $\lambda(x) = 1/(1 + \exp(-x))$, the inverse logit. Because $\lambda(x) = 1 - \lambda(-x)$,
$$\beta_0 + \beta_1 \lambda(x) = (\beta_0 + \beta_1) - \beta_1 \lambda(-x)).$$
Therefore it is imposs... | Estimating effect of latent variable in regression
One answer is "no." Another is, "of course."
No
To simplify notation, let $\lambda(x) = 1/(1 + \exp(-x))$, the inverse logit. Because $\lambda(x) = 1 - \lambda(-x)$,
$$\beta_0 + \beta_1 \lambda(x) = |
36,166 | Using an SVM for feature selection | As I understand them, SVMs have built-in regularization because they tend to penalize large weights of predictors which amounts to favor simpler models. They're often used with recursive feature elimination (in neuroimaging paradigms, at least).
About R specifically, there's the kernlab package, by Alex Smola who co-au... | Using an SVM for feature selection | As I understand them, SVMs have built-in regularization because they tend to penalize large weights of predictors which amounts to favor simpler models. They're often used with recursive feature elimi | Using an SVM for feature selection
As I understand them, SVMs have built-in regularization because they tend to penalize large weights of predictors which amounts to favor simpler models. They're often used with recursive feature elimination (in neuroimaging paradigms, at least).
About R specifically, there's the kernl... | Using an SVM for feature selection
As I understand them, SVMs have built-in regularization because they tend to penalize large weights of predictors which amounts to favor simpler models. They're often used with recursive feature elimi |
36,167 | Using an SVM for feature selection | For Recursive Feature Extraction (SVM-RFE) the packages e1071 and Kernlab doesn't implement it i think. For the Weka SVMAttributeEval package is for Java i think, but the question was for R as i saw.
The best way is trying to implement the SVM-RFE using e1071 and LIBSVM library
I found a good parper relating that here... | Using an SVM for feature selection | For Recursive Feature Extraction (SVM-RFE) the packages e1071 and Kernlab doesn't implement it i think. For the Weka SVMAttributeEval package is for Java i think, but the question was for R as i saw.
| Using an SVM for feature selection
For Recursive Feature Extraction (SVM-RFE) the packages e1071 and Kernlab doesn't implement it i think. For the Weka SVMAttributeEval package is for Java i think, but the question was for R as i saw.
The best way is trying to implement the SVM-RFE using e1071 and LIBSVM library
I fou... | Using an SVM for feature selection
For Recursive Feature Extraction (SVM-RFE) the packages e1071 and Kernlab doesn't implement it i think. For the Weka SVMAttributeEval package is for Java i think, but the question was for R as i saw.
|
36,168 | Using an SVM for feature selection | The Weka SVMAttributeEval package allows you to do feature selection using SVM.
It should be pretty easy to dump your R data frame to a csv file, import that into Weka, do the feature selection, and then pull it back into R. | Using an SVM for feature selection | The Weka SVMAttributeEval package allows you to do feature selection using SVM.
It should be pretty easy to dump your R data frame to a csv file, import that into Weka, do the feature selection, and t | Using an SVM for feature selection
The Weka SVMAttributeEval package allows you to do feature selection using SVM.
It should be pretty easy to dump your R data frame to a csv file, import that into Weka, do the feature selection, and then pull it back into R. | Using an SVM for feature selection
The Weka SVMAttributeEval package allows you to do feature selection using SVM.
It should be pretty easy to dump your R data frame to a csv file, import that into Weka, do the feature selection, and t |
36,169 | How to understand moments for a random variable? | If you have a linear rod, the center of gravity is the first moment (the expected value), and the moment of rotational inertia about the center of gravity is the variance. (A rod with centrally located mass will have less inertia than a rod with heavy concentrations of mass at the tips.) | How to understand moments for a random variable? | If you have a linear rod, the center of gravity is the first moment (the expected value), and the moment of rotational inertia about the center of gravity is the variance. (A rod with centrally locat | How to understand moments for a random variable?
If you have a linear rod, the center of gravity is the first moment (the expected value), and the moment of rotational inertia about the center of gravity is the variance. (A rod with centrally located mass will have less inertia than a rod with heavy concentrations of ... | How to understand moments for a random variable?
If you have a linear rod, the center of gravity is the first moment (the expected value), and the moment of rotational inertia about the center of gravity is the variance. (A rod with centrally locat |
36,170 | How to understand moments for a random variable? | Moments gives information about the statistical distribution. We judge one dataset over other based on moments of the dataset (e.g. difference between means(1st moment) of the 2 dataset) | How to understand moments for a random variable? | Moments gives information about the statistical distribution. We judge one dataset over other based on moments of the dataset (e.g. difference between means(1st moment) of the 2 dataset) | How to understand moments for a random variable?
Moments gives information about the statistical distribution. We judge one dataset over other based on moments of the dataset (e.g. difference between means(1st moment) of the 2 dataset) | How to understand moments for a random variable?
Moments gives information about the statistical distribution. We judge one dataset over other based on moments of the dataset (e.g. difference between means(1st moment) of the 2 dataset) |
36,171 | Market mix modelling with R | Marketing Mix Modelling is regression analysis with two differences. Variables are transformed to incorporate the memory effect of advertising, i.e. adstock effect, as well as diminishing returns of advertising.
I have created a simple tutorial on how to do Marketing Mix Modelling "MMM" in R:
https://analyticsartist.w... | Market mix modelling with R | Marketing Mix Modelling is regression analysis with two differences. Variables are transformed to incorporate the memory effect of advertising, i.e. adstock effect, as well as diminishing returns of a | Market mix modelling with R
Marketing Mix Modelling is regression analysis with two differences. Variables are transformed to incorporate the memory effect of advertising, i.e. adstock effect, as well as diminishing returns of advertising.
I have created a simple tutorial on how to do Marketing Mix Modelling "MMM" in R... | Market mix modelling with R
Marketing Mix Modelling is regression analysis with two differences. Variables are transformed to incorporate the memory effect of advertising, i.e. adstock effect, as well as diminishing returns of a |
36,172 | Market mix modelling with R | Regarding the R part, which was not cited at commentaries above:
bayesm R package, published nearly one year after this question was posted.
Title: Bayesian Inference for Marketing/Micro-econometrics.
Author: Peter Rossi . | Market mix modelling with R | Regarding the R part, which was not cited at commentaries above:
bayesm R package, published nearly one year after this question was posted.
Title: Bayesian Inference for Marketing/Micro-econometrics. | Market mix modelling with R
Regarding the R part, which was not cited at commentaries above:
bayesm R package, published nearly one year after this question was posted.
Title: Bayesian Inference for Marketing/Micro-econometrics.
Author: Peter Rossi . | Market mix modelling with R
Regarding the R part, which was not cited at commentaries above:
bayesm R package, published nearly one year after this question was posted.
Title: Bayesian Inference for Marketing/Micro-econometrics. |
36,173 | How can I demonstrate non-linearity without categorising a predictor? | Converting a continuous variable into categorical may be a bad idea, but may be a good idea as well, this depends on the problem. When the relationships of the variable can be best described using thresholds, categorisation may be one of the best options.
You wrote that in different categories of X1 the correlation bet... | How can I demonstrate non-linearity without categorising a predictor? | Converting a continuous variable into categorical may be a bad idea, but may be a good idea as well, this depends on the problem. When the relationships of the variable can be best described using thr | How can I demonstrate non-linearity without categorising a predictor?
Converting a continuous variable into categorical may be a bad idea, but may be a good idea as well, this depends on the problem. When the relationships of the variable can be best described using thresholds, categorisation may be one of the best opt... | How can I demonstrate non-linearity without categorising a predictor?
Converting a continuous variable into categorical may be a bad idea, but may be a good idea as well, this depends on the problem. When the relationships of the variable can be best described using thr |
36,174 | How can I demonstrate non-linearity without categorising a predictor? | You could fit a Generalized Additive Model (GAM) which could uncover nonlinear covariate effects quite easily.
In R you can use the gam or mgcv packages.
Here is the canonical reference | How can I demonstrate non-linearity without categorising a predictor? | You could fit a Generalized Additive Model (GAM) which could uncover nonlinear covariate effects quite easily.
In R you can use the gam or mgcv packages.
Here is the canonical reference | How can I demonstrate non-linearity without categorising a predictor?
You could fit a Generalized Additive Model (GAM) which could uncover nonlinear covariate effects quite easily.
In R you can use the gam or mgcv packages.
Here is the canonical reference | How can I demonstrate non-linearity without categorising a predictor?
You could fit a Generalized Additive Model (GAM) which could uncover nonlinear covariate effects quite easily.
In R you can use the gam or mgcv packages.
Here is the canonical reference |
36,175 | Introduction to maths for a junior in epidemiology | Jeff Gill has a good book, on Essential Mathematics for Social and Political Research: http://www.amazon.com/Essential-Mathematics-Political-Research-Analytical/dp/052168403X/ref=sr_1_2?ie=UTF8&s=books&qid=1301047912&sr=8-2
I found it quite useful for getting a good overview of linear algebra and calculus. He only assu... | Introduction to maths for a junior in epidemiology | Jeff Gill has a good book, on Essential Mathematics for Social and Political Research: http://www.amazon.com/Essential-Mathematics-Political-Research-Analytical/dp/052168403X/ref=sr_1_2?ie=UTF8&s=book | Introduction to maths for a junior in epidemiology
Jeff Gill has a good book, on Essential Mathematics for Social and Political Research: http://www.amazon.com/Essential-Mathematics-Political-Research-Analytical/dp/052168403X/ref=sr_1_2?ie=UTF8&s=books&qid=1301047912&sr=8-2
I found it quite useful for getting a good ov... | Introduction to maths for a junior in epidemiology
Jeff Gill has a good book, on Essential Mathematics for Social and Political Research: http://www.amazon.com/Essential-Mathematics-Political-Research-Analytical/dp/052168403X/ref=sr_1_2?ie=UTF8&s=book |
36,176 | Introduction to maths for a junior in epidemiology | As far as learning the information on slide 6 in the slideshow you linked to, I would suggest A Mathematical Primer for Social Statistics by John Fox (not free but cheap, Google book link). All of those sage green books are aimed at individuals with only a very brief statistics background.
If you are interested in taki... | Introduction to maths for a junior in epidemiology | As far as learning the information on slide 6 in the slideshow you linked to, I would suggest A Mathematical Primer for Social Statistics by John Fox (not free but cheap, Google book link). All of tho | Introduction to maths for a junior in epidemiology
As far as learning the information on slide 6 in the slideshow you linked to, I would suggest A Mathematical Primer for Social Statistics by John Fox (not free but cheap, Google book link). All of those sage green books are aimed at individuals with only a very brief s... | Introduction to maths for a junior in epidemiology
As far as learning the information on slide 6 in the slideshow you linked to, I would suggest A Mathematical Primer for Social Statistics by John Fox (not free but cheap, Google book link). All of tho |
36,177 | Logistic regression for bounds different from 0 and 1 | To begin with, I think we have to distinguish between logistic regression and (generalized) logistic function. Though the latter may be viewed as a separate case of a the former taking the time as the only explanatory variable. It is then straightforward to see that fitted process will go by $S$ shaped path that goes t... | Logistic regression for bounds different from 0 and 1 | To begin with, I think we have to distinguish between logistic regression and (generalized) logistic function. Though the latter may be viewed as a separate case of a the former taking the time as the | Logistic regression for bounds different from 0 and 1
To begin with, I think we have to distinguish between logistic regression and (generalized) logistic function. Though the latter may be viewed as a separate case of a the former taking the time as the only explanatory variable. It is then straightforward to see that... | Logistic regression for bounds different from 0 and 1
To begin with, I think we have to distinguish between logistic regression and (generalized) logistic function. Though the latter may be viewed as a separate case of a the former taking the time as the |
36,178 | Logistic regression for bounds different from 0 and 1 | You are looking for the wrong keywords. Logistic regression is for 0-1 outcomes, where the probability of being 1 is modeled with an S-shaped (logistic) function, not the actual data points themselves. Looking for nonlinear regression, specifically the four-parameter logistic model: $y = A + (B-A)/(1+\exp(-(a+bx))) + \... | Logistic regression for bounds different from 0 and 1 | You are looking for the wrong keywords. Logistic regression is for 0-1 outcomes, where the probability of being 1 is modeled with an S-shaped (logistic) function, not the actual data points themselves | Logistic regression for bounds different from 0 and 1
You are looking for the wrong keywords. Logistic regression is for 0-1 outcomes, where the probability of being 1 is modeled with an S-shaped (logistic) function, not the actual data points themselves. Looking for nonlinear regression, specifically the four-paramete... | Logistic regression for bounds different from 0 and 1
You are looking for the wrong keywords. Logistic regression is for 0-1 outcomes, where the probability of being 1 is modeled with an S-shaped (logistic) function, not the actual data points themselves |
36,179 | Which correlation measure should be used with a large gap (missing data)? | Create a scatterplot to check whether it makes any sense to suppose that a single correlation coefficient is an adequate description of the association between the variables.
For example, in these (simulated) data the correlation for ages 6-20 is 90%, for ages 50+ it's -70%, and overall it's 15%. In such a situation r... | Which correlation measure should be used with a large gap (missing data)? | Create a scatterplot to check whether it makes any sense to suppose that a single correlation coefficient is an adequate description of the association between the variables.
For example, in these (si | Which correlation measure should be used with a large gap (missing data)?
Create a scatterplot to check whether it makes any sense to suppose that a single correlation coefficient is an adequate description of the association between the variables.
For example, in these (simulated) data the correlation for ages 6-20 is... | Which correlation measure should be used with a large gap (missing data)?
Create a scatterplot to check whether it makes any sense to suppose that a single correlation coefficient is an adequate description of the association between the variables.
For example, in these (si |
36,180 | Generate random strings based on regular expressions in R | While generating random data from regular expressions would be a convenient interface, it is not directly supported in R. You could try one level of indirection though: generate random numbers and convert them into strings. For example, to convert a number into a character, you could use the following:
> rawToChar(as.r... | Generate random strings based on regular expressions in R | While generating random data from regular expressions would be a convenient interface, it is not directly supported in R. You could try one level of indirection though: generate random numbers and con | Generate random strings based on regular expressions in R
While generating random data from regular expressions would be a convenient interface, it is not directly supported in R. You could try one level of indirection though: generate random numbers and convert them into strings. For example, to convert a number into ... | Generate random strings based on regular expressions in R
While generating random data from regular expressions would be a convenient interface, it is not directly supported in R. You could try one level of indirection though: generate random numbers and con |
36,181 | Generate random strings based on regular expressions in R | Still not a perfect answer, however, Mark Heckmann has suggested using a random string generator which partially solves this problem:
GenRandomString <- function(n=1, lenght=12)
{
randomString <- c(1:n) # initialize vector
for (i in 1:n)
{
randomString[i] <- paste(sample(c(0:9, letters, LETTE... | Generate random strings based on regular expressions in R | Still not a perfect answer, however, Mark Heckmann has suggested using a random string generator which partially solves this problem:
GenRandomString <- function(n=1, lenght=12)
{
randomString <- c( | Generate random strings based on regular expressions in R
Still not a perfect answer, however, Mark Heckmann has suggested using a random string generator which partially solves this problem:
GenRandomString <- function(n=1, lenght=12)
{
randomString <- c(1:n) # initialize vector
for (i in 1:n)
{... | Generate random strings based on regular expressions in R
Still not a perfect answer, however, Mark Heckmann has suggested using a random string generator which partially solves this problem:
GenRandomString <- function(n=1, lenght=12)
{
randomString <- c( |
36,182 | Can one use Cohen's Kappa for two judgements only? | The "chance correction" in Cohen's $\kappa$ estimates probabilities with which each rater chooses the existing categories. The estimation comes from the marginal frequencies of the categories. When you only have 1 judgement for each rater, this means that $\kappa$ assumes the category chosen for this single judgement i... | Can one use Cohen's Kappa for two judgements only? | The "chance correction" in Cohen's $\kappa$ estimates probabilities with which each rater chooses the existing categories. The estimation comes from the marginal frequencies of the categories. When yo | Can one use Cohen's Kappa for two judgements only?
The "chance correction" in Cohen's $\kappa$ estimates probabilities with which each rater chooses the existing categories. The estimation comes from the marginal frequencies of the categories. When you only have 1 judgement for each rater, this means that $\kappa$ assu... | Can one use Cohen's Kappa for two judgements only?
The "chance correction" in Cohen's $\kappa$ estimates probabilities with which each rater chooses the existing categories. The estimation comes from the marginal frequencies of the categories. When yo |
36,183 | Can one use Cohen's Kappa for two judgements only? | I find caracal's answer convincing, but I also believe Cohen's Kappa can only account for part of what constitutes interrater reliability. The simple % of ratings in agreement accounts for another part, and the correlation between ratings, a third. It takes all three methods to gain a complete picture. For details p... | Can one use Cohen's Kappa for two judgements only? | I find caracal's answer convincing, but I also believe Cohen's Kappa can only account for part of what constitutes interrater reliability. The simple % of ratings in agreement accounts for another pa | Can one use Cohen's Kappa for two judgements only?
I find caracal's answer convincing, but I also believe Cohen's Kappa can only account for part of what constitutes interrater reliability. The simple % of ratings in agreement accounts for another part, and the correlation between ratings, a third. It takes all three... | Can one use Cohen's Kappa for two judgements only?
I find caracal's answer convincing, but I also believe Cohen's Kappa can only account for part of what constitutes interrater reliability. The simple % of ratings in agreement accounts for another pa |
36,184 | Test for independence, when I'm missing one bin of a 2x2 contingency | If the missing count were close to 22*12/17, the table would appear independent. This is consistent with your observations. If the missing count is far from this value, the table would exhibit strong lack of dependence. This, too, is consistent with your observations. Evidently your data cannot discriminate the two... | Test for independence, when I'm missing one bin of a 2x2 contingency | If the missing count were close to 22*12/17, the table would appear independent. This is consistent with your observations. If the missing count is far from this value, the table would exhibit stron | Test for independence, when I'm missing one bin of a 2x2 contingency
If the missing count were close to 22*12/17, the table would appear independent. This is consistent with your observations. If the missing count is far from this value, the table would exhibit strong lack of dependence. This, too, is consistent wit... | Test for independence, when I'm missing one bin of a 2x2 contingency
If the missing count were close to 22*12/17, the table would appear independent. This is consistent with your observations. If the missing count is far from this value, the table would exhibit stron |
36,185 | Use Empirical CDF vs Distribution CDF? | Personally, I'd favour instead showing the fit of the theoretical to the empirical distribution using a set of P-P plots or Q-Q plots. | Use Empirical CDF vs Distribution CDF? | Personally, I'd favour instead showing the fit of the theoretical to the empirical distribution using a set of P-P plots or Q-Q plots. | Use Empirical CDF vs Distribution CDF?
Personally, I'd favour instead showing the fit of the theoretical to the empirical distribution using a set of P-P plots or Q-Q plots. | Use Empirical CDF vs Distribution CDF?
Personally, I'd favour instead showing the fit of the theoretical to the empirical distribution using a set of P-P plots or Q-Q plots. |
36,186 | Use Empirical CDF vs Distribution CDF? | The empirical CDF needs to be treated with care at the end points of the data, and in other places where there is "sparse" data. This is because they tend to make weak structural assumptions about what goes on "in between" each data point. It would also be a good idea to have "dots" for the empirical CDF plot rather ... | Use Empirical CDF vs Distribution CDF? | The empirical CDF needs to be treated with care at the end points of the data, and in other places where there is "sparse" data. This is because they tend to make weak structural assumptions about wh | Use Empirical CDF vs Distribution CDF?
The empirical CDF needs to be treated with care at the end points of the data, and in other places where there is "sparse" data. This is because they tend to make weak structural assumptions about what goes on "in between" each data point. It would also be a good idea to have "d... | Use Empirical CDF vs Distribution CDF?
The empirical CDF needs to be treated with care at the end points of the data, and in other places where there is "sparse" data. This is because they tend to make weak structural assumptions about wh |
36,187 | Questions about antithetic variate method | Yes. There's no simple condition. When $f$ is monotonic, $f(X_1)$ and $f(Y_1)$ will still be negatively correlated. When $f$ is not monotonic, all bets are off. For example, let $F$ be a uniform distribution on $[-1,1]$ and let $f(x) = x^2$. Then $X_1 = -Y_1$, whence $f(X_1) = f(Y_1)$, implying $f(X_1)$ and $f(Y_1... | Questions about antithetic variate method | Yes. There's no simple condition. When $f$ is monotonic, $f(X_1)$ and $f(Y_1)$ will still be negatively correlated. When $f$ is not monotonic, all bets are off. For example, let $F$ be a uniform d | Questions about antithetic variate method
Yes. There's no simple condition. When $f$ is monotonic, $f(X_1)$ and $f(Y_1)$ will still be negatively correlated. When $f$ is not monotonic, all bets are off. For example, let $F$ be a uniform distribution on $[-1,1]$ and let $f(x) = x^2$. Then $X_1 = -Y_1$, whence $f(X_... | Questions about antithetic variate method
Yes. There's no simple condition. When $f$ is monotonic, $f(X_1)$ and $f(Y_1)$ will still be negatively correlated. When $f$ is not monotonic, all bets are off. For example, let $F$ be a uniform d |
36,188 | Can we estimate the size of a subset X of a set A, by randomly sampling subsets of A? | OK, try reading the wikipedia page for Monte Carlo integration. You'll see they mention a stratified version. Stratification is the technical term in statistics for what you attempt: subdividing in subsets (subsamples). I guess the references can help you further. | Can we estimate the size of a subset X of a set A, by randomly sampling subsets of A? | OK, try reading the wikipedia page for Monte Carlo integration. You'll see they mention a stratified version. Stratification is the technical term in statistics for what you attempt: subdividing in su | Can we estimate the size of a subset X of a set A, by randomly sampling subsets of A?
OK, try reading the wikipedia page for Monte Carlo integration. You'll see they mention a stratified version. Stratification is the technical term in statistics for what you attempt: subdividing in subsets (subsamples). I guess the re... | Can we estimate the size of a subset X of a set A, by randomly sampling subsets of A?
OK, try reading the wikipedia page for Monte Carlo integration. You'll see they mention a stratified version. Stratification is the technical term in statistics for what you attempt: subdividing in su |
36,189 | Can we estimate the size of a subset X of a set A, by randomly sampling subsets of A? | For any subset $Y$ of $A$, let $\pi(Y)$ be the probability you will select it in your sampling. You have described a random variable
$$f(Y) = |Y \cap X|.$$
The total of $f$ in the population of subsets of $A$ is
$$\tau(X) = \sum_{Y \subset A}|Y \cap X| = 2^{|A|-1}|X|.$$
From a sample (with replacement) of subsets of... | Can we estimate the size of a subset X of a set A, by randomly sampling subsets of A? | For any subset $Y$ of $A$, let $\pi(Y)$ be the probability you will select it in your sampling. You have described a random variable
$$f(Y) = |Y \cap X|.$$
The total of $f$ in the population of subs | Can we estimate the size of a subset X of a set A, by randomly sampling subsets of A?
For any subset $Y$ of $A$, let $\pi(Y)$ be the probability you will select it in your sampling. You have described a random variable
$$f(Y) = |Y \cap X|.$$
The total of $f$ in the population of subsets of $A$ is
$$\tau(X) = \sum_{Y ... | Can we estimate the size of a subset X of a set A, by randomly sampling subsets of A?
For any subset $Y$ of $A$, let $\pi(Y)$ be the probability you will select it in your sampling. You have described a random variable
$$f(Y) = |Y \cap X|.$$
The total of $f$ in the population of subs |
36,190 | Can we estimate the size of a subset X of a set A, by randomly sampling subsets of A? | I assume your measure is finite. WLOG it can be a probability.
The first procedure you mention is the good old empirical probability estimate:
$\hat{P}(Y\in X)= | \{ x_i \in X\} | /n $
(there montecarlo estimate of an inetgral is a good interpretation also). In high dimension it does not work since $\{x_i\in X\}$ is... | Can we estimate the size of a subset X of a set A, by randomly sampling subsets of A? | I assume your measure is finite. WLOG it can be a probability.
The first procedure you mention is the good old empirical probability estimate:
$\hat{P}(Y\in X)= | \{ x_i \in X\} | /n $
(there monte | Can we estimate the size of a subset X of a set A, by randomly sampling subsets of A?
I assume your measure is finite. WLOG it can be a probability.
The first procedure you mention is the good old empirical probability estimate:
$\hat{P}(Y\in X)= | \{ x_i \in X\} | /n $
(there montecarlo estimate of an inetgral is a... | Can we estimate the size of a subset X of a set A, by randomly sampling subsets of A?
I assume your measure is finite. WLOG it can be a probability.
The first procedure you mention is the good old empirical probability estimate:
$\hat{P}(Y\in X)= | \{ x_i \in X\} | /n $
(there monte |
36,191 | Averaging time series with different sampling interval | The zoo package is very good at that (as is xts which extends it). The zoo vignettes have e.g. this example:
zr3 <- zooreg(rnorm(9), start=as.yearmon(2000), frequency=12)
zr3
aggregate(zr3, as.yearqtr, mean)
A (regular) series is created with monthly frequency, and the averaged by quarter. It works the very same way ... | Averaging time series with different sampling interval | The zoo package is very good at that (as is xts which extends it). The zoo vignettes have e.g. this example:
zr3 <- zooreg(rnorm(9), start=as.yearmon(2000), frequency=12)
zr3
aggregate(zr3, as.yearqt | Averaging time series with different sampling interval
The zoo package is very good at that (as is xts which extends it). The zoo vignettes have e.g. this example:
zr3 <- zooreg(rnorm(9), start=as.yearmon(2000), frequency=12)
zr3
aggregate(zr3, as.yearqtr, mean)
A (regular) series is created with monthly frequency, a... | Averaging time series with different sampling interval
The zoo package is very good at that (as is xts which extends it). The zoo vignettes have e.g. this example:
zr3 <- zooreg(rnorm(9), start=as.yearmon(2000), frequency=12)
zr3
aggregate(zr3, as.yearqt |
36,192 | Video/Audio online material for getting into Bayesian analysis and logistic-regressions | I would go straight to VideoLectures.net. This is by far the best source--whether free or paid--i have found for very-high quality (both w/r/t the video quality and w/r/t the presentation content) video lectures and tutorials on statistics, forecasting, and machine learning. The target audience for these video lectures... | Video/Audio online material for getting into Bayesian analysis and logistic-regressions | I would go straight to VideoLectures.net. This is by far the best source--whether free or paid--i have found for very-high quality (both w/r/t the video quality and w/r/t the presentation content) vid | Video/Audio online material for getting into Bayesian analysis and logistic-regressions
I would go straight to VideoLectures.net. This is by far the best source--whether free or paid--i have found for very-high quality (both w/r/t the video quality and w/r/t the presentation content) video lectures and tutorials on sta... | Video/Audio online material for getting into Bayesian analysis and logistic-regressions
I would go straight to VideoLectures.net. This is by far the best source--whether free or paid--i have found for very-high quality (both w/r/t the video quality and w/r/t the presentation content) vid |
36,193 | Video/Audio online material for getting into Bayesian analysis and logistic-regressions | I've only had a little look at this lecture series on Machine Learning, but it looks good.
http://academicearth.org/courses/machine-learning
Lecture 11 covers Bayesian Statistics and Regularization. | Video/Audio online material for getting into Bayesian analysis and logistic-regressions | I've only had a little look at this lecture series on Machine Learning, but it looks good.
http://academicearth.org/courses/machine-learning
Lecture 11 covers Bayesian Statistics and Regularization. | Video/Audio online material for getting into Bayesian analysis and logistic-regressions
I've only had a little look at this lecture series on Machine Learning, but it looks good.
http://academicearth.org/courses/machine-learning
Lecture 11 covers Bayesian Statistics and Regularization. | Video/Audio online material for getting into Bayesian analysis and logistic-regressions
I've only had a little look at this lecture series on Machine Learning, but it looks good.
http://academicearth.org/courses/machine-learning
Lecture 11 covers Bayesian Statistics and Regularization. |
36,194 | Video/Audio online material for getting into Bayesian analysis and logistic-regressions | try the machine learning summer school La Palma 2012
http://www.youtube.com/channel/UCHhbDEKA7BP58mq1wfTBQNQ?feature=watch
impressive indeed | Video/Audio online material for getting into Bayesian analysis and logistic-regressions | try the machine learning summer school La Palma 2012
http://www.youtube.com/channel/UCHhbDEKA7BP58mq1wfTBQNQ?feature=watch
impressive indeed | Video/Audio online material for getting into Bayesian analysis and logistic-regressions
try the machine learning summer school La Palma 2012
http://www.youtube.com/channel/UCHhbDEKA7BP58mq1wfTBQNQ?feature=watch
impressive indeed | Video/Audio online material for getting into Bayesian analysis and logistic-regressions
try the machine learning summer school La Palma 2012
http://www.youtube.com/channel/UCHhbDEKA7BP58mq1wfTBQNQ?feature=watch
impressive indeed |
36,195 | Video/Audio online material for getting into Bayesian analysis and logistic-regressions | Coursera is offering a wide a range of online lectures. The Machine Learning lecture by Andrew Ng covers logistic regression and regularization in the beginning. Furthermore, Probabilistic Graphical Models by Daphne Koller might be of interest to you as well. | Video/Audio online material for getting into Bayesian analysis and logistic-regressions | Coursera is offering a wide a range of online lectures. The Machine Learning lecture by Andrew Ng covers logistic regression and regularization in the beginning. Furthermore, Probabilistic Graphical M | Video/Audio online material for getting into Bayesian analysis and logistic-regressions
Coursera is offering a wide a range of online lectures. The Machine Learning lecture by Andrew Ng covers logistic regression and regularization in the beginning. Furthermore, Probabilistic Graphical Models by Daphne Koller might be ... | Video/Audio online material for getting into Bayesian analysis and logistic-regressions
Coursera is offering a wide a range of online lectures. The Machine Learning lecture by Andrew Ng covers logistic regression and regularization in the beginning. Furthermore, Probabilistic Graphical M |
36,196 | Prove $Y=X$ almost surely given they have the same distribution and $Y$ is an increasing function of $X$ | The idea is clear in a picture: if we were to sketch the graph of the distribution function $F_X$ of $X,$ we may conceive of $g$ as locally shifting all horizontal points various amounts (but always consistently, never allowing any overlapping), thereby distorting the graph of $F_X$ into the graph of $F_Y$. The condit... | Prove $Y=X$ almost surely given they have the same distribution and $Y$ is an increasing function of | The idea is clear in a picture: if we were to sketch the graph of the distribution function $F_X$ of $X,$ we may conceive of $g$ as locally shifting all horizontal points various amounts (but always c | Prove $Y=X$ almost surely given they have the same distribution and $Y$ is an increasing function of $X$
The idea is clear in a picture: if we were to sketch the graph of the distribution function $F_X$ of $X,$ we may conceive of $g$ as locally shifting all horizontal points various amounts (but always consistently, ne... | Prove $Y=X$ almost surely given they have the same distribution and $Y$ is an increasing function of
The idea is clear in a picture: if we were to sketch the graph of the distribution function $F_X$ of $X,$ we may conceive of $g$ as locally shifting all horizontal points various amounts (but always c |
36,197 | Radial axis transformation in polar kernel density estimate | Consider any density $f$ for the circular parameter $\theta.$ The relevant integrals are of the form $$\Pr(\mathcal A) = \int_\mathcal{A}f(\theta)\,\mathrm d\theta$$ where $\mathcal A\subset[0,2\pi)$ is any circular event. Ordinarily we would plot them in Cartesian coordinates, as in this example:
Now, if you wish to... | Radial axis transformation in polar kernel density estimate | Consider any density $f$ for the circular parameter $\theta.$ The relevant integrals are of the form $$\Pr(\mathcal A) = \int_\mathcal{A}f(\theta)\,\mathrm d\theta$$ where $\mathcal A\subset[0,2\pi)$ | Radial axis transformation in polar kernel density estimate
Consider any density $f$ for the circular parameter $\theta.$ The relevant integrals are of the form $$\Pr(\mathcal A) = \int_\mathcal{A}f(\theta)\,\mathrm d\theta$$ where $\mathcal A\subset[0,2\pi)$ is any circular event. Ordinarily we would plot them in Car... | Radial axis transformation in polar kernel density estimate
Consider any density $f$ for the circular parameter $\theta.$ The relevant integrals are of the form $$\Pr(\mathcal A) = \int_\mathcal{A}f(\theta)\,\mathrm d\theta$$ where $\mathcal A\subset[0,2\pi)$ |
36,198 | How to model the probability of a truth claim given an arrangement of eyewitness accounts supporting specific instances of that claim? | TL;DR
The paper Holder, Hume on Miracles: Bayesian Interpretation, Multiple Testimony, and the Existence of God deals with Bayesian updating based on witness reports from multiple events. Holder only considers the case of two events, so I will adapt his calculations to the general case.
I expected, intuitively, that (o... | How to model the probability of a truth claim given an arrangement of eyewitness accounts supporting | TL;DR
The paper Holder, Hume on Miracles: Bayesian Interpretation, Multiple Testimony, and the Existence of God deals with Bayesian updating based on witness reports from multiple events. Holder only | How to model the probability of a truth claim given an arrangement of eyewitness accounts supporting specific instances of that claim?
TL;DR
The paper Holder, Hume on Miracles: Bayesian Interpretation, Multiple Testimony, and the Existence of God deals with Bayesian updating based on witness reports from multiple event... | How to model the probability of a truth claim given an arrangement of eyewitness accounts supporting
TL;DR
The paper Holder, Hume on Miracles: Bayesian Interpretation, Multiple Testimony, and the Existence of God deals with Bayesian updating based on witness reports from multiple events. Holder only |
36,199 | How to model the probability of a truth claim given an arrangement of eyewitness accounts supporting specific instances of that claim? | You use an expression like 'P(E1 is true | N eyewitness accounts for E1)', but I assume that the question is not whether E1 is true but instead whether X is true.
Instead you might consider the probability that $X$ is true given the number of eyewitnesses $N_1, N_2, etc.$
$$P(\text{$X$ true} | N_1, N_2, \dots, N_k)$$
T... | How to model the probability of a truth claim given an arrangement of eyewitness accounts supporting | You use an expression like 'P(E1 is true | N eyewitness accounts for E1)', but I assume that the question is not whether E1 is true but instead whether X is true.
Instead you might consider the probab | How to model the probability of a truth claim given an arrangement of eyewitness accounts supporting specific instances of that claim?
You use an expression like 'P(E1 is true | N eyewitness accounts for E1)', but I assume that the question is not whether E1 is true but instead whether X is true.
Instead you might cons... | How to model the probability of a truth claim given an arrangement of eyewitness accounts supporting
You use an expression like 'P(E1 is true | N eyewitness accounts for E1)', but I assume that the question is not whether E1 is true but instead whether X is true.
Instead you might consider the probab |
36,200 | How to model the probability of a truth claim given an arrangement of eyewitness accounts supporting specific instances of that claim? | There’s a trade off based on the correlations among observers, with a possible quantitative argument and a clearer qualitative case.
A quantitative approach might consider $n$ observers in $k$ correlated groups, where the correlations relate the probability of an event observed by A and B with the probability of an eve... | How to model the probability of a truth claim given an arrangement of eyewitness accounts supporting | There’s a trade off based on the correlations among observers, with a possible quantitative argument and a clearer qualitative case.
A quantitative approach might consider $n$ observers in $k$ correla | How to model the probability of a truth claim given an arrangement of eyewitness accounts supporting specific instances of that claim?
There’s a trade off based on the correlations among observers, with a possible quantitative argument and a clearer qualitative case.
A quantitative approach might consider $n$ observers... | How to model the probability of a truth claim given an arrangement of eyewitness accounts supporting
There’s a trade off based on the correlations among observers, with a possible quantitative argument and a clearer qualitative case.
A quantitative approach might consider $n$ observers in $k$ correla |
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