idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
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43,301 | How does number of observations supporting alternate hypothesis on a test of a variance have to scale so that null is rejected? | People please correct me, but I have the impression that, given the set up, this is a simple problem that does not even require asymptotic theory, because, as it is posed, involves an alternative situation (the "malfunctioning" case) that possibly affects the assumption on the distribution family also (in case the dist... | How does number of observations supporting alternate hypothesis on a test of a variance have to scal | People please correct me, but I have the impression that, given the set up, this is a simple problem that does not even require asymptotic theory, because, as it is posed, involves an alternative situ | How does number of observations supporting alternate hypothesis on a test of a variance have to scale so that null is rejected?
People please correct me, but I have the impression that, given the set up, this is a simple problem that does not even require asymptotic theory, because, as it is posed, involves an alternat... | How does number of observations supporting alternate hypothesis on a test of a variance have to scal
People please correct me, but I have the impression that, given the set up, this is a simple problem that does not even require asymptotic theory, because, as it is posed, involves an alternative situ |
43,302 | Estimating the functional form of the slowly time-varying variance of a Gaussian process | In functional data analysis, people often use penalties of the form
$$ \int_{D} [f^{(m)}(x)]^{2} dx $$
to ensure that an estimate of $f$ is smooth. Here $D$ is the domain of the function and $f^{(m)}(x)$ is the $m$'th derivative of $f$. In my own research I've found $m = 2$ to be useful. In your case the log-likeliho... | Estimating the functional form of the slowly time-varying variance of a Gaussian process | In functional data analysis, people often use penalties of the form
$$ \int_{D} [f^{(m)}(x)]^{2} dx $$
to ensure that an estimate of $f$ is smooth. Here $D$ is the domain of the function and $f^{(m) | Estimating the functional form of the slowly time-varying variance of a Gaussian process
In functional data analysis, people often use penalties of the form
$$ \int_{D} [f^{(m)}(x)]^{2} dx $$
to ensure that an estimate of $f$ is smooth. Here $D$ is the domain of the function and $f^{(m)}(x)$ is the $m$'th derivative ... | Estimating the functional form of the slowly time-varying variance of a Gaussian process
In functional data analysis, people often use penalties of the form
$$ \int_{D} [f^{(m)}(x)]^{2} dx $$
to ensure that an estimate of $f$ is smooth. Here $D$ is the domain of the function and $f^{(m) |
43,303 | What is the best way to use a 2-class classifier for a multi-category case? | Training c linear discriminant functions is a example of a "1-vs-all" or "1-against-the-rest" approach to building a multiclass classifier given a binary classifier learning algorithm. Training C(c,2) 2-class classifiers is an example of the "1-vs-1" approach. As c gets larger, the "1-vs-1" approach builds a lot more... | What is the best way to use a 2-class classifier for a multi-category case? | Training c linear discriminant functions is a example of a "1-vs-all" or "1-against-the-rest" approach to building a multiclass classifier given a binary classifier learning algorithm. Training C(c,2 | What is the best way to use a 2-class classifier for a multi-category case?
Training c linear discriminant functions is a example of a "1-vs-all" or "1-against-the-rest" approach to building a multiclass classifier given a binary classifier learning algorithm. Training C(c,2) 2-class classifiers is an example of the "... | What is the best way to use a 2-class classifier for a multi-category case?
Training c linear discriminant functions is a example of a "1-vs-all" or "1-against-the-rest" approach to building a multiclass classifier given a binary classifier learning algorithm. Training C(c,2 |
43,304 | How to estimate the absolute expected difference? | Bootstrap bias correction was invented to adjusted for bias in the estimation of $f(Z)$ (my $Z$ is your $X-Y$). The idea is very simple: create $B$ bootstrap resamples from your data, and calculate $f_b=f(Z_b)$ for each one of them. Then the bootrstrap estimate of the bias is $\bar{f_b}-f(Z)$, where $\bar{f_b}$ is the... | How to estimate the absolute expected difference? | Bootstrap bias correction was invented to adjusted for bias in the estimation of $f(Z)$ (my $Z$ is your $X-Y$). The idea is very simple: create $B$ bootstrap resamples from your data, and calculate $f | How to estimate the absolute expected difference?
Bootstrap bias correction was invented to adjusted for bias in the estimation of $f(Z)$ (my $Z$ is your $X-Y$). The idea is very simple: create $B$ bootstrap resamples from your data, and calculate $f_b=f(Z_b)$ for each one of them. Then the bootrstrap estimate of the b... | How to estimate the absolute expected difference?
Bootstrap bias correction was invented to adjusted for bias in the estimation of $f(Z)$ (my $Z$ is your $X-Y$). The idea is very simple: create $B$ bootstrap resamples from your data, and calculate $f |
43,305 | Creating point clusters of a specified size using R | Give the spatstat package a go - the package was designed by CSIRO for spatial point pattern analysis. There's a very extensive paper going over the use of the package on the CSIRO website. | Creating point clusters of a specified size using R | Give the spatstat package a go - the package was designed by CSIRO for spatial point pattern analysis. There's a very extensive paper going over the use of the package on the CSIRO website. | Creating point clusters of a specified size using R
Give the spatstat package a go - the package was designed by CSIRO for spatial point pattern analysis. There's a very extensive paper going over the use of the package on the CSIRO website. | Creating point clusters of a specified size using R
Give the spatstat package a go - the package was designed by CSIRO for spatial point pattern analysis. There's a very extensive paper going over the use of the package on the CSIRO website. |
43,306 | Creating point clusters of a specified size using R | How different are your cluster sizes with plain k-means ?
Take a look at
k-means-algorithm-variation-with-equal-cluster-size (Python, not R). | Creating point clusters of a specified size using R | How different are your cluster sizes with plain k-means ?
Take a look at
k-means-algorithm-variation-with-equal-cluster-size (Python, not R). | Creating point clusters of a specified size using R
How different are your cluster sizes with plain k-means ?
Take a look at
k-means-algorithm-variation-with-equal-cluster-size (Python, not R). | Creating point clusters of a specified size using R
How different are your cluster sizes with plain k-means ?
Take a look at
k-means-algorithm-variation-with-equal-cluster-size (Python, not R). |
43,307 | What are the uses and pitfalls of regression through the origin? [duplicate] | To me the main issue boils down to imposing a strong constraint on an unknown process.
Consider a specification $y_t=f(x_t)+\varepsilon_t$. If you don't know the exact form of a function $f(.)$, you could try a linear approximation: $$f(z)\approx a+b x_t$$
Notice, how this linear approximation is actually the first or... | What are the uses and pitfalls of regression through the origin? [duplicate] | To me the main issue boils down to imposing a strong constraint on an unknown process.
Consider a specification $y_t=f(x_t)+\varepsilon_t$. If you don't know the exact form of a function $f(.)$, you | What are the uses and pitfalls of regression through the origin? [duplicate]
To me the main issue boils down to imposing a strong constraint on an unknown process.
Consider a specification $y_t=f(x_t)+\varepsilon_t$. If you don't know the exact form of a function $f(.)$, you could try a linear approximation: $$f(z)\ap... | What are the uses and pitfalls of regression through the origin? [duplicate]
To me the main issue boils down to imposing a strong constraint on an unknown process.
Consider a specification $y_t=f(x_t)+\varepsilon_t$. If you don't know the exact form of a function $f(.)$, you |
43,308 | What are the uses and pitfalls of regression through the origin? [duplicate] | If the r.h.s variables & response have not been centered? Then (by definition) the estimated coefficients are biased. | What are the uses and pitfalls of regression through the origin? [duplicate] | If the r.h.s variables & response have not been centered? Then (by definition) the estimated coefficients are biased. | What are the uses and pitfalls of regression through the origin? [duplicate]
If the r.h.s variables & response have not been centered? Then (by definition) the estimated coefficients are biased. | What are the uses and pitfalls of regression through the origin? [duplicate]
If the r.h.s variables & response have not been centered? Then (by definition) the estimated coefficients are biased. |
43,309 | What are the uses and pitfalls of regression through the origin? [duplicate] | The least-squares solution to the set of equations
0 = c1*x1_1 + c2*x1_2 + ... cn*x1_n
0 = c1*x2_1 + c2*x2_2 + ... cn*x2_n
0 = c1*x3_1 + c2*x3_2 + ... cn*x3_n
...
0 = c1*xn_1 + c2*xn_2 + ... cn*xn_n
is always c1=0, c2=0, ..., with zero error, so using standard tools, eg. the Perl module Statistics::Regression, to... | What are the uses and pitfalls of regression through the origin? [duplicate] | The least-squares solution to the set of equations
0 = c1*x1_1 + c2*x1_2 + ... cn*x1_n
0 = c1*x2_1 + c2*x2_2 + ... cn*x2_n
0 = c1*x3_1 + c2*x3_2 + ... cn*x3_n
...
0 = c1*xn_1 + c2*xn_2 + ... cn*x | What are the uses and pitfalls of regression through the origin? [duplicate]
The least-squares solution to the set of equations
0 = c1*x1_1 + c2*x1_2 + ... cn*x1_n
0 = c1*x2_1 + c2*x2_2 + ... cn*x2_n
0 = c1*x3_1 + c2*x3_2 + ... cn*x3_n
...
0 = c1*xn_1 + c2*xn_2 + ... cn*xn_n
is always c1=0, c2=0, ..., with zero e... | What are the uses and pitfalls of regression through the origin? [duplicate]
The least-squares solution to the set of equations
0 = c1*x1_1 + c2*x1_2 + ... cn*x1_n
0 = c1*x2_1 + c2*x2_2 + ... cn*x2_n
0 = c1*x3_1 + c2*x3_2 + ... cn*x3_n
...
0 = c1*xn_1 + c2*xn_2 + ... cn*x |
43,310 | Species Richness, Dominance and Diversity Differences | I think neither of these responses fit perfectly into any of the standard GLM link functions. Taking a pragmatic approach it is probably sufficient to pick a link function that is broadly doing the right thing.
Your raw data is from a multinomial distribution, and Richness, $R$, is the number of categories with a scor... | Species Richness, Dominance and Diversity Differences | I think neither of these responses fit perfectly into any of the standard GLM link functions. Taking a pragmatic approach it is probably sufficient to pick a link function that is broadly doing the r | Species Richness, Dominance and Diversity Differences
I think neither of these responses fit perfectly into any of the standard GLM link functions. Taking a pragmatic approach it is probably sufficient to pick a link function that is broadly doing the right thing.
Your raw data is from a multinomial distribution, and ... | Species Richness, Dominance and Diversity Differences
I think neither of these responses fit perfectly into any of the standard GLM link functions. Taking a pragmatic approach it is probably sufficient to pick a link function that is broadly doing the r |
43,311 | Explanation for the thresholds in the sequential probability ratio test | A first step in understanding this type of testing plan is to consider a Double-Sampling Plan for attributes. This type of plan is designed to determine whether a lot of product should be accepted or rejected based on sampling items, where each item in the lot can be categorized as either good or defective. The plan is... | Explanation for the thresholds in the sequential probability ratio test | A first step in understanding this type of testing plan is to consider a Double-Sampling Plan for attributes. This type of plan is designed to determine whether a lot of product should be accepted or | Explanation for the thresholds in the sequential probability ratio test
A first step in understanding this type of testing plan is to consider a Double-Sampling Plan for attributes. This type of plan is designed to determine whether a lot of product should be accepted or rejected based on sampling items, where each ite... | Explanation for the thresholds in the sequential probability ratio test
A first step in understanding this type of testing plan is to consider a Double-Sampling Plan for attributes. This type of plan is designed to determine whether a lot of product should be accepted or |
43,312 | Standardized residuals vs. regular residuals | The $u$s are unobserved and the $\hat{d}$s are just estimates of them. | Standardized residuals vs. regular residuals | The $u$s are unobserved and the $\hat{d}$s are just estimates of them. | Standardized residuals vs. regular residuals
The $u$s are unobserved and the $\hat{d}$s are just estimates of them. | Standardized residuals vs. regular residuals
The $u$s are unobserved and the $\hat{d}$s are just estimates of them. |
43,313 | Standardized residuals vs. regular residuals | This inference is no different from any other inference we make. We assume a default (you could call it a 'null'). In this case, it's that the underlying distribution is Gaussian. We examine the data to see if they are inconsistent with our default hypothesis. If the qq-plot of our residuals looks sufficiently Gaus... | Standardized residuals vs. regular residuals | This inference is no different from any other inference we make. We assume a default (you could call it a 'null'). In this case, it's that the underlying distribution is Gaussian. We examine the da | Standardized residuals vs. regular residuals
This inference is no different from any other inference we make. We assume a default (you could call it a 'null'). In this case, it's that the underlying distribution is Gaussian. We examine the data to see if they are inconsistent with our default hypothesis. If the qq-... | Standardized residuals vs. regular residuals
This inference is no different from any other inference we make. We assume a default (you could call it a 'null'). In this case, it's that the underlying distribution is Gaussian. We examine the da |
43,314 | Standardized residuals vs. regular residuals | In a linear model $ y = X\beta + u $ with $u \sim N(0, \sigma^2I)$, the vector of raw residuals is $ \hat u = y - \hat y = (I - H)y $ where the hat matrix $H = X(X'X)^{-1}X' $. The response $y$ is normally distributed given the assumed normality of the error terms.
Consequently, if the model assumes normality corre... | Standardized residuals vs. regular residuals | In a linear model $ y = X\beta + u $ with $u \sim N(0, \sigma^2I)$, the vector of raw residuals is $ \hat u = y - \hat y = (I - H)y $ where the hat matrix $H = X(X'X)^{-1}X' $. The response $y$ is | Standardized residuals vs. regular residuals
In a linear model $ y = X\beta + u $ with $u \sim N(0, \sigma^2I)$, the vector of raw residuals is $ \hat u = y - \hat y = (I - H)y $ where the hat matrix $H = X(X'X)^{-1}X' $. The response $y$ is normally distributed given the assumed normality of the error terms.
Conse... | Standardized residuals vs. regular residuals
In a linear model $ y = X\beta + u $ with $u \sim N(0, \sigma^2I)$, the vector of raw residuals is $ \hat u = y - \hat y = (I - H)y $ where the hat matrix $H = X(X'X)^{-1}X' $. The response $y$ is |
43,315 | Classifying clusters using discriminant analysis | A good idea might be to run some ANOVAS and MANOVAS on the cluster for whatever variables you're using. The variables that generated the cluster should generally yield significant differences, but if the 5 new vars you're incorporating were not the vars you used to generate the cluster solution, it's interesting to run... | Classifying clusters using discriminant analysis | A good idea might be to run some ANOVAS and MANOVAS on the cluster for whatever variables you're using. The variables that generated the cluster should generally yield significant differences, but if | Classifying clusters using discriminant analysis
A good idea might be to run some ANOVAS and MANOVAS on the cluster for whatever variables you're using. The variables that generated the cluster should generally yield significant differences, but if the 5 new vars you're incorporating were not the vars you used to gener... | Classifying clusters using discriminant analysis
A good idea might be to run some ANOVAS and MANOVAS on the cluster for whatever variables you're using. The variables that generated the cluster should generally yield significant differences, but if |
43,316 | Inverse of false discovery rate (FDR) | You are trying to find the 'rejection region' for a given $q_{crit}$. ($q_{crit}$ is typically referred to as $\alpha$ in the literature.)
Further Reading:
Storey, JD "A direct approach to false discovery rates" J. R. Statist. Soc. (2002)
www.genomine.org/papers/directfdr.pdf | Inverse of false discovery rate (FDR) | You are trying to find the 'rejection region' for a given $q_{crit}$. ($q_{crit}$ is typically referred to as $\alpha$ in the literature.)
Further Reading:
Storey, JD "A direct approach to false disco | Inverse of false discovery rate (FDR)
You are trying to find the 'rejection region' for a given $q_{crit}$. ($q_{crit}$ is typically referred to as $\alpha$ in the literature.)
Further Reading:
Storey, JD "A direct approach to false discovery rates" J. R. Statist. Soc. (2002)
www.genomine.org/papers/directfdr.pdf | Inverse of false discovery rate (FDR)
You are trying to find the 'rejection region' for a given $q_{crit}$. ($q_{crit}$ is typically referred to as $\alpha$ in the literature.)
Further Reading:
Storey, JD "A direct approach to false disco |
43,317 | How to test whether two distance/difference matrices are different? | I am not sure I understand what you mean by difference/distance/dissimilarity matrix. Assuming that $D_{i,j}^2 = (v_i - v_j)^{\top}(v_i - v_j)$ for some vectors $v_i, v_j$, if you can accept a transformation to the crossproduct matrix $G_{i,j} = -2 v_i^{\top}v_j$ (say for example the vectors are normalized so $v_i^{\to... | How to test whether two distance/difference matrices are different? | I am not sure I understand what you mean by difference/distance/dissimilarity matrix. Assuming that $D_{i,j}^2 = (v_i - v_j)^{\top}(v_i - v_j)$ for some vectors $v_i, v_j$, if you can accept a transfo | How to test whether two distance/difference matrices are different?
I am not sure I understand what you mean by difference/distance/dissimilarity matrix. Assuming that $D_{i,j}^2 = (v_i - v_j)^{\top}(v_i - v_j)$ for some vectors $v_i, v_j$, if you can accept a transformation to the crossproduct matrix $G_{i,j} = -2 v_i... | How to test whether two distance/difference matrices are different?
I am not sure I understand what you mean by difference/distance/dissimilarity matrix. Assuming that $D_{i,j}^2 = (v_i - v_j)^{\top}(v_i - v_j)$ for some vectors $v_i, v_j$, if you can accept a transfo |
43,318 | Which hierarchical clustering algorithm? | Sounds like you need HAC (hierarchical agglomerative clustering). There are many variants, but the basic idea is that you start with singleton clusters and progressively merge, based on different ways of determining which clusters are the "closest".
For more on HAC, see the wikipedia entry. | Which hierarchical clustering algorithm? | Sounds like you need HAC (hierarchical agglomerative clustering). There are many variants, but the basic idea is that you start with singleton clusters and progressively merge, based on different ways | Which hierarchical clustering algorithm?
Sounds like you need HAC (hierarchical agglomerative clustering). There are many variants, but the basic idea is that you start with singleton clusters and progressively merge, based on different ways of determining which clusters are the "closest".
For more on HAC, see the wik... | Which hierarchical clustering algorithm?
Sounds like you need HAC (hierarchical agglomerative clustering). There are many variants, but the basic idea is that you start with singleton clusters and progressively merge, based on different ways |
43,319 | Which hierarchical clustering algorithm? | Some interesting things you could try out:
Take a look at SigClust - it's an R function that allows you to establish the significance of clustering using bootstrapping/monte carlo simulation. SigClust will provide a p-value for the clustering operation between two sets of points. Theoretically, you can run it at every... | Which hierarchical clustering algorithm? | Some interesting things you could try out:
Take a look at SigClust - it's an R function that allows you to establish the significance of clustering using bootstrapping/monte carlo simulation. SigClus | Which hierarchical clustering algorithm?
Some interesting things you could try out:
Take a look at SigClust - it's an R function that allows you to establish the significance of clustering using bootstrapping/monte carlo simulation. SigClust will provide a p-value for the clustering operation between two sets of point... | Which hierarchical clustering algorithm?
Some interesting things you could try out:
Take a look at SigClust - it's an R function that allows you to establish the significance of clustering using bootstrapping/monte carlo simulation. SigClus |
43,320 | Which hierarchical clustering algorithm? | You might want to try "model-based clustering". This algorithm uses "BIC" to determine the number of clusters.
Sincerely | Which hierarchical clustering algorithm? | You might want to try "model-based clustering". This algorithm uses "BIC" to determine the number of clusters.
Sincerely | Which hierarchical clustering algorithm?
You might want to try "model-based clustering". This algorithm uses "BIC" to determine the number of clusters.
Sincerely | Which hierarchical clustering algorithm?
You might want to try "model-based clustering". This algorithm uses "BIC" to determine the number of clusters.
Sincerely |
43,321 | Canonical correlation analysis and time series analysis | I don't think that using CCA will help you. It appears to me that you have a number of endogenous series ( abundance of species n in number ) and a number of exogenous series ( variety of food resources m in number ). I would suggest constructing n transfer functions each one optimized to fully utilize the information ... | Canonical correlation analysis and time series analysis | I don't think that using CCA will help you. It appears to me that you have a number of endogenous series ( abundance of species n in number ) and a number of exogenous series ( variety of food resourc | Canonical correlation analysis and time series analysis
I don't think that using CCA will help you. It appears to me that you have a number of endogenous series ( abundance of species n in number ) and a number of exogenous series ( variety of food resources m in number ). I would suggest constructing n transfer functi... | Canonical correlation analysis and time series analysis
I don't think that using CCA will help you. It appears to me that you have a number of endogenous series ( abundance of species n in number ) and a number of exogenous series ( variety of food resourc |
43,322 | Hard exemplary problem sets to work through to solidify my understanding of statistical concepts? | Mathematical Statistics and Data Analysis, by John A. Rice, Third Edition
If you are struggling to understand how different things in statistical relate to each other, the "glue" that you are missing is an understanding of mathematical statistics.
Rice's textbook provides the theory that justifies most of the statistic... | Hard exemplary problem sets to work through to solidify my understanding of statistical concepts? | Mathematical Statistics and Data Analysis, by John A. Rice, Third Edition
If you are struggling to understand how different things in statistical relate to each other, the "glue" that you are missing | Hard exemplary problem sets to work through to solidify my understanding of statistical concepts?
Mathematical Statistics and Data Analysis, by John A. Rice, Third Edition
If you are struggling to understand how different things in statistical relate to each other, the "glue" that you are missing is an understanding of... | Hard exemplary problem sets to work through to solidify my understanding of statistical concepts?
Mathematical Statistics and Data Analysis, by John A. Rice, Third Edition
If you are struggling to understand how different things in statistical relate to each other, the "glue" that you are missing |
43,323 | Two-way robust ANOVA | How is normality violated? Medians are more sensitive to skew than means as n gets low. Be careful of that. It would be very problematic if small n's varied in a systematic way.
How much is homoscedascity violated? If the n's are about equal it won't matter much for quite large differences. | Two-way robust ANOVA | How is normality violated? Medians are more sensitive to skew than means as n gets low. Be careful of that. It would be very problematic if small n's varied in a systematic way.
How much is homosce | Two-way robust ANOVA
How is normality violated? Medians are more sensitive to skew than means as n gets low. Be careful of that. It would be very problematic if small n's varied in a systematic way.
How much is homoscedascity violated? If the n's are about equal it won't matter much for quite large differences. | Two-way robust ANOVA
How is normality violated? Medians are more sensitive to skew than means as n gets low. Be careful of that. It would be very problematic if small n's varied in a systematic way.
How much is homosce |
43,324 | How would life expectancy impact the calculation of disease prevalence? | It depends on the disease. Consider the following formula:
Prevalence = Incidence * Duration.
If the disease state is permanent, like HIV, then a decrease in life expectancy will decrease prevalence. Example:
We have a population of 1000 people. 5 people get the disease, so incidence = 0.005. If the disease is harmless... | How would life expectancy impact the calculation of disease prevalence? | It depends on the disease. Consider the following formula:
Prevalence = Incidence * Duration.
If the disease state is permanent, like HIV, then a decrease in life expectancy will decrease prevalence. | How would life expectancy impact the calculation of disease prevalence?
It depends on the disease. Consider the following formula:
Prevalence = Incidence * Duration.
If the disease state is permanent, like HIV, then a decrease in life expectancy will decrease prevalence. Example:
We have a population of 1000 people. 5 ... | How would life expectancy impact the calculation of disease prevalence?
It depends on the disease. Consider the following formula:
Prevalence = Incidence * Duration.
If the disease state is permanent, like HIV, then a decrease in life expectancy will decrease prevalence. |
43,325 | Derivation of distance in TwoStep clustering | SPSS two step cluster model algorithm is described in more detail in:
Chiu, Tom, DongPing Fang, John Chen, Yao Wang, and Christopher Jeris (2001), "A robust and scalable clustering algorithm for mixed type attributes in large database environment," Proceedings of the seventh ACM SIGKDD international conference on Knowl... | Derivation of distance in TwoStep clustering | SPSS two step cluster model algorithm is described in more detail in:
Chiu, Tom, DongPing Fang, John Chen, Yao Wang, and Christopher Jeris (2001), "A robust and scalable clustering algorithm for mixed | Derivation of distance in TwoStep clustering
SPSS two step cluster model algorithm is described in more detail in:
Chiu, Tom, DongPing Fang, John Chen, Yao Wang, and Christopher Jeris (2001), "A robust and scalable clustering algorithm for mixed type attributes in large database environment," Proceedings of the seventh... | Derivation of distance in TwoStep clustering
SPSS two step cluster model algorithm is described in more detail in:
Chiu, Tom, DongPing Fang, John Chen, Yao Wang, and Christopher Jeris (2001), "A robust and scalable clustering algorithm for mixed |
43,326 | Example of discontinous effect of x on y dataset (for paper) | In economics, this is called "regression discontinuity." For one example, check out
David Card & Carlos Dobkin & Nicole Maestas, 2008. "The Impact of Nearly Universal Insurance Coverage on Health Care Utilization: Evidence from Medicare," American Economic Review, American Economic Association, vol. 98(5), pages 2242-5... | Example of discontinous effect of x on y dataset (for paper) | In economics, this is called "regression discontinuity." For one example, check out
David Card & Carlos Dobkin & Nicole Maestas, 2008. "The Impact of Nearly Universal Insurance Coverage on Health Care | Example of discontinous effect of x on y dataset (for paper)
In economics, this is called "regression discontinuity." For one example, check out
David Card & Carlos Dobkin & Nicole Maestas, 2008. "The Impact of Nearly Universal Insurance Coverage on Health Care Utilization: Evidence from Medicare," American Economic Re... | Example of discontinous effect of x on y dataset (for paper)
In economics, this is called "regression discontinuity." For one example, check out
David Card & Carlos Dobkin & Nicole Maestas, 2008. "The Impact of Nearly Universal Insurance Coverage on Health Care |
43,327 | How do I calculate the effect size for the Kolmogorov-Smirnov Z statistic? | Yes. $D = Z/\sqrt{n}$ for the one-sample test. $D = Z/\sqrt{\frac{n_1 n_2}{n_1 + n_2}}$ for the two-sample test. $D$ should also be the "Most Extreme Differences - Absolute" entry in the output graphic (double-click the table shown in the SPSS output viewer). $Z$ might be labeled "Test Statistic," "Kolmogorov-Smirnov Z... | How do I calculate the effect size for the Kolmogorov-Smirnov Z statistic? | Yes. $D = Z/\sqrt{n}$ for the one-sample test. $D = Z/\sqrt{\frac{n_1 n_2}{n_1 + n_2}}$ for the two-sample test. $D$ should also be the "Most Extreme Differences - Absolute" entry in the output graphi | How do I calculate the effect size for the Kolmogorov-Smirnov Z statistic?
Yes. $D = Z/\sqrt{n}$ for the one-sample test. $D = Z/\sqrt{\frac{n_1 n_2}{n_1 + n_2}}$ for the two-sample test. $D$ should also be the "Most Extreme Differences - Absolute" entry in the output graphic (double-click the table shown in the SPSS o... | How do I calculate the effect size for the Kolmogorov-Smirnov Z statistic?
Yes. $D = Z/\sqrt{n}$ for the one-sample test. $D = Z/\sqrt{\frac{n_1 n_2}{n_1 + n_2}}$ for the two-sample test. $D$ should also be the "Most Extreme Differences - Absolute" entry in the output graphi |
43,328 | Merging spatial and temporal clusters | I don’t know if I understood your question correctly, but I’ll give it a try.
Any hierarchical agglomerative algorithm can do the job. Remember that agglomerative algorithms proceed by “pasting” observations to a cluster and treating the cluster as a single unit.
I would suggest this:
Substitute your 1d or 2d observ... | Merging spatial and temporal clusters | I don’t know if I understood your question correctly, but I’ll give it a try.
Any hierarchical agglomerative algorithm can do the job. Remember that agglomerative algorithms proceed by “pasting” obser | Merging spatial and temporal clusters
I don’t know if I understood your question correctly, but I’ll give it a try.
Any hierarchical agglomerative algorithm can do the job. Remember that agglomerative algorithms proceed by “pasting” observations to a cluster and treating the cluster as a single unit.
I would suggest t... | Merging spatial and temporal clusters
I don’t know if I understood your question correctly, but I’ll give it a try.
Any hierarchical agglomerative algorithm can do the job. Remember that agglomerative algorithms proceed by “pasting” obser |
43,329 | Can we get confidence intervals for entries in stationary vector for an absorbing, time-independent Markov chain? | So, as said in the comments, the Markov chain you consider has some absorbing states (and is irreducible, presumably), hence its stationary distribution is concentrated on these absorbing states. Therefore the issue is to compute some confidence intervals for the only two non zero coordinates of the stationary vector, ... | Can we get confidence intervals for entries in stationary vector for an absorbing, time-independent | So, as said in the comments, the Markov chain you consider has some absorbing states (and is irreducible, presumably), hence its stationary distribution is concentrated on these absorbing states. Ther | Can we get confidence intervals for entries in stationary vector for an absorbing, time-independent Markov chain?
So, as said in the comments, the Markov chain you consider has some absorbing states (and is irreducible, presumably), hence its stationary distribution is concentrated on these absorbing states. Therefore ... | Can we get confidence intervals for entries in stationary vector for an absorbing, time-independent
So, as said in the comments, the Markov chain you consider has some absorbing states (and is irreducible, presumably), hence its stationary distribution is concentrated on these absorbing states. Ther |
43,330 | Is a correlation analysis with Pearson's correlation and Bonferroni Method a valid approach to find correlations between two sets of data | If you want to test that a given correlation coefficient is significantly different from 0 you would use the distribution of the sample Pearson product moment correlation under the null hypothesis. What they are asking here is different. In a specific case they use the hypergeometric distribution because if there is r... | Is a correlation analysis with Pearson's correlation and Bonferroni Method a valid approach to find | If you want to test that a given correlation coefficient is significantly different from 0 you would use the distribution of the sample Pearson product moment correlation under the null hypothesis. Wh | Is a correlation analysis with Pearson's correlation and Bonferroni Method a valid approach to find correlations between two sets of data
If you want to test that a given correlation coefficient is significantly different from 0 you would use the distribution of the sample Pearson product moment correlation under the n... | Is a correlation analysis with Pearson's correlation and Bonferroni Method a valid approach to find
If you want to test that a given correlation coefficient is significantly different from 0 you would use the distribution of the sample Pearson product moment correlation under the null hypothesis. Wh |
43,331 | Choosing the scope when performing multiple comparisons? | Think of the following two experiments:
Experiment A; Throw a fair coin 10 times to assess Prob(Heads).
Experiment B: Throw a fair dice 5 times to assess Prob(Face showing 1).
To take the coin toss example from the wiki: We may wish to declare a coin as biased if we observe more than 9 heads out of 10 tosses.
Thus, if... | Choosing the scope when performing multiple comparisons? | Think of the following two experiments:
Experiment A; Throw a fair coin 10 times to assess Prob(Heads).
Experiment B: Throw a fair dice 5 times to assess Prob(Face showing 1).
To take the coin toss ex | Choosing the scope when performing multiple comparisons?
Think of the following two experiments:
Experiment A; Throw a fair coin 10 times to assess Prob(Heads).
Experiment B: Throw a fair dice 5 times to assess Prob(Face showing 1).
To take the coin toss example from the wiki: We may wish to declare a coin as biased if... | Choosing the scope when performing multiple comparisons?
Think of the following two experiments:
Experiment A; Throw a fair coin 10 times to assess Prob(Heads).
Experiment B: Throw a fair dice 5 times to assess Prob(Face showing 1).
To take the coin toss ex |
43,332 | What is the expected number of runs of same color in a standard deck of cards? | Suppose $X_n$ denotes the color of the $n$th card in the shuffled deck.
Then note that the last card always denotes the end of a run. Other ends of runs are characterized by $X_n\ne X_{n+1}$ which indicates a run ending at $n$.
Note that $P(X_n\ne X_{n+1})=26/51$ (since once you fix a card, you can choose another card ... | What is the expected number of runs of same color in a standard deck of cards? | Suppose $X_n$ denotes the color of the $n$th card in the shuffled deck.
Then note that the last card always denotes the end of a run. Other ends of runs are characterized by $X_n\ne X_{n+1}$ which ind | What is the expected number of runs of same color in a standard deck of cards?
Suppose $X_n$ denotes the color of the $n$th card in the shuffled deck.
Then note that the last card always denotes the end of a run. Other ends of runs are characterized by $X_n\ne X_{n+1}$ which indicates a run ending at $n$.
Note that $P(... | What is the expected number of runs of same color in a standard deck of cards?
Suppose $X_n$ denotes the color of the $n$th card in the shuffled deck.
Then note that the last card always denotes the end of a run. Other ends of runs are characterized by $X_n\ne X_{n+1}$ which ind |
43,333 | Why prediction of a predicted variable from a discriminant analysis is imperfect | This is quite normal in case of machine learning -- it does not need to be optimal, it must be general. | Why prediction of a predicted variable from a discriminant analysis is imperfect | This is quite normal in case of machine learning -- it does not need to be optimal, it must be general. | Why prediction of a predicted variable from a discriminant analysis is imperfect
This is quite normal in case of machine learning -- it does not need to be optimal, it must be general. | Why prediction of a predicted variable from a discriminant analysis is imperfect
This is quite normal in case of machine learning -- it does not need to be optimal, it must be general. |
43,334 | Why prediction of a predicted variable from a discriminant analysis is imperfect | I am having troubling following your reasoning, but here are some things you should consider.
Generally, the harder you fit a model to your training data, the worse the model will perform on independent validation data sets. By over-fitting the model to the training set, you risk capturing predictor-response relationsh... | Why prediction of a predicted variable from a discriminant analysis is imperfect | I am having troubling following your reasoning, but here are some things you should consider.
Generally, the harder you fit a model to your training data, the worse the model will perform on independe | Why prediction of a predicted variable from a discriminant analysis is imperfect
I am having troubling following your reasoning, but here are some things you should consider.
Generally, the harder you fit a model to your training data, the worse the model will perform on independent validation data sets. By over-fittin... | Why prediction of a predicted variable from a discriminant analysis is imperfect
I am having troubling following your reasoning, but here are some things you should consider.
Generally, the harder you fit a model to your training data, the worse the model will perform on independe |
43,335 | Why do Bayesians care about the frequentist properties of Bayesian credible intervals? | Instead the correct interpretation of confidence intervals seem to be upon repeated samples of the data from the likelihood ('repeated experiments'), (1−α)
of the confidence intervals generated (which will differ every experiment) will contain the true value θ
Your understanding of the confidence interval is complicat... | Why do Bayesians care about the frequentist properties of Bayesian credible intervals? | Instead the correct interpretation of confidence intervals seem to be upon repeated samples of the data from the likelihood ('repeated experiments'), (1−α)
of the confidence intervals generated (which | Why do Bayesians care about the frequentist properties of Bayesian credible intervals?
Instead the correct interpretation of confidence intervals seem to be upon repeated samples of the data from the likelihood ('repeated experiments'), (1−α)
of the confidence intervals generated (which will differ every experiment) wi... | Why do Bayesians care about the frequentist properties of Bayesian credible intervals?
Instead the correct interpretation of confidence intervals seem to be upon repeated samples of the data from the likelihood ('repeated experiments'), (1−α)
of the confidence intervals generated (which |
43,336 | Selecting ARIMA orders by ACF/PACF vs. by information criteria | While there is limited published research directly comparing the Box-Jenkins method based on ACF/PACF plots and information criteria-based methods in ARIMA model selection, most researchers and practitioners have gravitated towards using information criteria methods, such as AIC or BIC, due to their increased efficienc... | Selecting ARIMA orders by ACF/PACF vs. by information criteria | While there is limited published research directly comparing the Box-Jenkins method based on ACF/PACF plots and information criteria-based methods in ARIMA model selection, most researchers and practi | Selecting ARIMA orders by ACF/PACF vs. by information criteria
While there is limited published research directly comparing the Box-Jenkins method based on ACF/PACF plots and information criteria-based methods in ARIMA model selection, most researchers and practitioners have gravitated towards using information criteri... | Selecting ARIMA orders by ACF/PACF vs. by information criteria
While there is limited published research directly comparing the Box-Jenkins method based on ACF/PACF plots and information criteria-based methods in ARIMA model selection, most researchers and practi |
43,337 | Selecting ARIMA orders by ACF/PACF vs. by information criteria | From my experience, finding ARIMA (p) and (q) parameters from the ACF/PACF plots yields better results than auto.arima, measured as lower RSME and/or lower MAPE errors. Note that ARIMA is a statistical model, and the output of the auto-correlation plots relate directly to the auto-regression (AR) and moving average (MA... | Selecting ARIMA orders by ACF/PACF vs. by information criteria | From my experience, finding ARIMA (p) and (q) parameters from the ACF/PACF plots yields better results than auto.arima, measured as lower RSME and/or lower MAPE errors. Note that ARIMA is a statistica | Selecting ARIMA orders by ACF/PACF vs. by information criteria
From my experience, finding ARIMA (p) and (q) parameters from the ACF/PACF plots yields better results than auto.arima, measured as lower RSME and/or lower MAPE errors. Note that ARIMA is a statistical model, and the output of the auto-correlation plots rel... | Selecting ARIMA orders by ACF/PACF vs. by information criteria
From my experience, finding ARIMA (p) and (q) parameters from the ACF/PACF plots yields better results than auto.arima, measured as lower RSME and/or lower MAPE errors. Note that ARIMA is a statistica |
43,338 | Adam is an adaptive learning rate method, why people decrease its learning rate manually? | There is no one-size-fits-all optimizer. Adam sometimes works, sometimes doesn't. If you look at what is used in different research papers, you would see different optimizers used. There are some authors who argue that you should use vanilla SGD. Adam's learning rate may need tuning and is not necessarily the best algo... | Adam is an adaptive learning rate method, why people decrease its learning rate manually? | There is no one-size-fits-all optimizer. Adam sometimes works, sometimes doesn't. If you look at what is used in different research papers, you would see different optimizers used. There are some auth | Adam is an adaptive learning rate method, why people decrease its learning rate manually?
There is no one-size-fits-all optimizer. Adam sometimes works, sometimes doesn't. If you look at what is used in different research papers, you would see different optimizers used. There are some authors who argue that you should ... | Adam is an adaptive learning rate method, why people decrease its learning rate manually?
There is no one-size-fits-all optimizer. Adam sometimes works, sometimes doesn't. If you look at what is used in different research papers, you would see different optimizers used. There are some auth |
43,339 | Rule-Based and Tree-Based Statistical Models | Rule-based classifier cannot by greedy by definition, IMHO, since they involve only 1 "node". Hence, any rule-based classifier should fulfil what you need. Then, it depends what are your data/task. For classification I would suggest arulesCBA as the first choice to try:
https://cran.r-project.org/web/packages/arulesCBA... | Rule-Based and Tree-Based Statistical Models | Rule-based classifier cannot by greedy by definition, IMHO, since they involve only 1 "node". Hence, any rule-based classifier should fulfil what you need. Then, it depends what are your data/task. Fo | Rule-Based and Tree-Based Statistical Models
Rule-based classifier cannot by greedy by definition, IMHO, since they involve only 1 "node". Hence, any rule-based classifier should fulfil what you need. Then, it depends what are your data/task. For classification I would suggest arulesCBA as the first choice to try:
http... | Rule-Based and Tree-Based Statistical Models
Rule-based classifier cannot by greedy by definition, IMHO, since they involve only 1 "node". Hence, any rule-based classifier should fulfil what you need. Then, it depends what are your data/task. Fo |
43,340 | Analyzing Pfizer Vaccine Efficacy: Testing a Claim about 2 Proportions | Based on the comments, there are several issues with this analysis. First, the analysis is based on being able to approximate binomial as normal. Textbooks I've consulted write this requirement in different ways. Sullivan (Fundamentals of Statistics) says that in addition to being binomial, each of the groups must pa... | Analyzing Pfizer Vaccine Efficacy: Testing a Claim about 2 Proportions | Based on the comments, there are several issues with this analysis. First, the analysis is based on being able to approximate binomial as normal. Textbooks I've consulted write this requirement in d | Analyzing Pfizer Vaccine Efficacy: Testing a Claim about 2 Proportions
Based on the comments, there are several issues with this analysis. First, the analysis is based on being able to approximate binomial as normal. Textbooks I've consulted write this requirement in different ways. Sullivan (Fundamentals of Statisti... | Analyzing Pfizer Vaccine Efficacy: Testing a Claim about 2 Proportions
Based on the comments, there are several issues with this analysis. First, the analysis is based on being able to approximate binomial as normal. Textbooks I've consulted write this requirement in d |
43,341 | How to train a neural network *not* to give a certain output? | Usually when doing multi-class classification, we encode the classes using one-hot encoding. For example, in four-class classification, belonging to third class would be encoded as [0, 0, 1, 0]. In your case, you seem to have missing information in the data, since you know only something like "it's not class one, or tw... | How to train a neural network *not* to give a certain output? | Usually when doing multi-class classification, we encode the classes using one-hot encoding. For example, in four-class classification, belonging to third class would be encoded as [0, 0, 1, 0]. In yo | How to train a neural network *not* to give a certain output?
Usually when doing multi-class classification, we encode the classes using one-hot encoding. For example, in four-class classification, belonging to third class would be encoded as [0, 0, 1, 0]. In your case, you seem to have missing information in the data,... | How to train a neural network *not* to give a certain output?
Usually when doing multi-class classification, we encode the classes using one-hot encoding. For example, in four-class classification, belonging to third class would be encoded as [0, 0, 1, 0]. In yo |
43,342 | How to train a neural network *not* to give a certain output? | To answer the question about your approach of providing target distributions: yes, that is a correct approach, and softmax is the right final layer for this approach. | How to train a neural network *not* to give a certain output? | To answer the question about your approach of providing target distributions: yes, that is a correct approach, and softmax is the right final layer for this approach. | How to train a neural network *not* to give a certain output?
To answer the question about your approach of providing target distributions: yes, that is a correct approach, and softmax is the right final layer for this approach. | How to train a neural network *not* to give a certain output?
To answer the question about your approach of providing target distributions: yes, that is a correct approach, and softmax is the right final layer for this approach. |
43,343 | When to test For Equality of Medians, and when Stochastic Equality? | This depends, in part, on the number of ordinal categories.
If the number of categories is small, then comparing the medians may be uninformative. Suppose the categories are Like/Like Somewhat/Neutral/Dislike Somewhat/Dislike, the
poor group’s answers are distributed 20%-20%-20%-20%-20%, and the rich group’s answers ar... | When to test For Equality of Medians, and when Stochastic Equality? | This depends, in part, on the number of ordinal categories.
If the number of categories is small, then comparing the medians may be uninformative. Suppose the categories are Like/Like Somewhat/Neutral | When to test For Equality of Medians, and when Stochastic Equality?
This depends, in part, on the number of ordinal categories.
If the number of categories is small, then comparing the medians may be uninformative. Suppose the categories are Like/Like Somewhat/Neutral/Dislike Somewhat/Dislike, the
poor group’s answers ... | When to test For Equality of Medians, and when Stochastic Equality?
This depends, in part, on the number of ordinal categories.
If the number of categories is small, then comparing the medians may be uninformative. Suppose the categories are Like/Like Somewhat/Neutral |
43,344 | Can any class of ML algorithms efficiently learn the modulo function (x mod y)? | FOURIER SERIES
"Not a machine learning algorithm," you say? I disagree. A Fourier series is a linear regression with infinite (nonlinear) features.
$$
\hat y_i = \hat a_0 + \hat a_1\cos\left(
\dfrac{
2\pi \times 1x_i
}{
P
}
\right)
+ \hat b_1\sin\left(
\dfrac{
2\pi \times 1x_i
}{
P
}
\right)
+ \hat a_2\cos\left(
\dfra... | Can any class of ML algorithms efficiently learn the modulo function (x mod y)? | FOURIER SERIES
"Not a machine learning algorithm," you say? I disagree. A Fourier series is a linear regression with infinite (nonlinear) features.
$$
\hat y_i = \hat a_0 + \hat a_1\cos\left(
\dfrac{
| Can any class of ML algorithms efficiently learn the modulo function (x mod y)?
FOURIER SERIES
"Not a machine learning algorithm," you say? I disagree. A Fourier series is a linear regression with infinite (nonlinear) features.
$$
\hat y_i = \hat a_0 + \hat a_1\cos\left(
\dfrac{
2\pi \times 1x_i
}{
P
}
\right)
+ \hat ... | Can any class of ML algorithms efficiently learn the modulo function (x mod y)?
FOURIER SERIES
"Not a machine learning algorithm," you say? I disagree. A Fourier series is a linear regression with infinite (nonlinear) features.
$$
\hat y_i = \hat a_0 + \hat a_1\cos\left(
\dfrac{
|
43,345 | Distribution-free confidence interval for IQR | Two observations, which may produce an acceptable result.
[EDIT] To answer the question on a theoretical formula, I start with individual sample quantiles, see presentation here, which assumes knowledge of the probability density function (pdf). Also, also this work which gives precise theoretical results for several d... | Distribution-free confidence interval for IQR | Two observations, which may produce an acceptable result.
[EDIT] To answer the question on a theoretical formula, I start with individual sample quantiles, see presentation here, which assumes knowled | Distribution-free confidence interval for IQR
Two observations, which may produce an acceptable result.
[EDIT] To answer the question on a theoretical formula, I start with individual sample quantiles, see presentation here, which assumes knowledge of the probability density function (pdf). Also, also this work which g... | Distribution-free confidence interval for IQR
Two observations, which may produce an acceptable result.
[EDIT] To answer the question on a theoretical formula, I start with individual sample quantiles, see presentation here, which assumes knowled |
43,346 | intraclass correlation (ICC) to assess interrater reliability with repeated measures in R | and of course, time is nested within patient
You could just as easily consider patients nested in occasions, if your research question were about differences between occasions. From the perspective of generalizability theory (GT), your repeated measures are really cross-classified. Your "G-study" design is fully cro... | intraclass correlation (ICC) to assess interrater reliability with repeated measures in R | and of course, time is nested within patient
You could just as easily consider patients nested in occasions, if your research question were about differences between occasions. From the perspective | intraclass correlation (ICC) to assess interrater reliability with repeated measures in R
and of course, time is nested within patient
You could just as easily consider patients nested in occasions, if your research question were about differences between occasions. From the perspective of generalizability theory (GT... | intraclass correlation (ICC) to assess interrater reliability with repeated measures in R
and of course, time is nested within patient
You could just as easily consider patients nested in occasions, if your research question were about differences between occasions. From the perspective |
43,347 | Probability of A given B or C | 1)
$P(A | B \text{ or } C)=P(A|B\cup C)=\frac{P(A\cap(B\cup C))}{P(B\cup C)}$
2)$P(A | B \text{ or } C \text{ or } \dots \color{red}{\text{or }X})$
what is $B \text{ or } X$?
If $X$ is a random variable, I think it is only valid if we use it like $B\cup \{X\in E\}=\{\omega \in \Omega \mid \omega \in B \text{ or } x(\o... | Probability of A given B or C | 1)
$P(A | B \text{ or } C)=P(A|B\cup C)=\frac{P(A\cap(B\cup C))}{P(B\cup C)}$
2)$P(A | B \text{ or } C \text{ or } \dots \color{red}{\text{or }X})$
what is $B \text{ or } X$?
If $X$ is a random variab | Probability of A given B or C
1)
$P(A | B \text{ or } C)=P(A|B\cup C)=\frac{P(A\cap(B\cup C))}{P(B\cup C)}$
2)$P(A | B \text{ or } C \text{ or } \dots \color{red}{\text{or }X})$
what is $B \text{ or } X$?
If $X$ is a random variable, I think it is only valid if we use it like $B\cup \{X\in E\}=\{\omega \in \Omega \mid... | Probability of A given B or C
1)
$P(A | B \text{ or } C)=P(A|B\cup C)=\frac{P(A\cap(B\cup C))}{P(B\cup C)}$
2)$P(A | B \text{ or } C \text{ or } \dots \color{red}{\text{or }X})$
what is $B \text{ or } X$?
If $X$ is a random variab |
43,348 | ANOVA: life after rejecting the null hypothesis | I'll try to answer one piece of the question. There is an established protocol for distinguishing similar groups from dissimilar groups which results in a compact letter display. A series of pairwise tests can be reduced to a compact letter display manually, or, for example, with the function cld in the multcomp pack... | ANOVA: life after rejecting the null hypothesis | I'll try to answer one piece of the question. There is an established protocol for distinguishing similar groups from dissimilar groups which results in a compact letter display. A series of pairwis | ANOVA: life after rejecting the null hypothesis
I'll try to answer one piece of the question. There is an established protocol for distinguishing similar groups from dissimilar groups which results in a compact letter display. A series of pairwise tests can be reduced to a compact letter display manually, or, for exa... | ANOVA: life after rejecting the null hypothesis
I'll try to answer one piece of the question. There is an established protocol for distinguishing similar groups from dissimilar groups which results in a compact letter display. A series of pairwis |
43,349 | ANOVA: life after rejecting the null hypothesis | The approaches that I have used to deal with this problem:
Pairwise comparisons There is much literature devoted to the statistical issues involved in post_hoc pairwise testing. In practical terms however pairwise testing itself does not give any answers: you still need some kind of clustering algorithm and post-post-... | ANOVA: life after rejecting the null hypothesis | The approaches that I have used to deal with this problem:
Pairwise comparisons There is much literature devoted to the statistical issues involved in post_hoc pairwise testing. In practical terms ho | ANOVA: life after rejecting the null hypothesis
The approaches that I have used to deal with this problem:
Pairwise comparisons There is much literature devoted to the statistical issues involved in post_hoc pairwise testing. In practical terms however pairwise testing itself does not give any answers: you still need ... | ANOVA: life after rejecting the null hypothesis
The approaches that I have used to deal with this problem:
Pairwise comparisons There is much literature devoted to the statistical issues involved in post_hoc pairwise testing. In practical terms ho |
43,350 | LASSO with poorly conditioned predictors | When you say you're solving $Ay = b$, you're automatically placing yourself into a normal OLS regression problem. Lasso simply does not belong to that kind of problem, which is why "the matrix being ill conditioned" has little meaning.
By solving a normal OLS, you're finding $b$ that minimizes $RSS(b)=\sum(y_i - b_0 -... | LASSO with poorly conditioned predictors | When you say you're solving $Ay = b$, you're automatically placing yourself into a normal OLS regression problem. Lasso simply does not belong to that kind of problem, which is why "the matrix being i | LASSO with poorly conditioned predictors
When you say you're solving $Ay = b$, you're automatically placing yourself into a normal OLS regression problem. Lasso simply does not belong to that kind of problem, which is why "the matrix being ill conditioned" has little meaning.
By solving a normal OLS, you're finding $b... | LASSO with poorly conditioned predictors
When you say you're solving $Ay = b$, you're automatically placing yourself into a normal OLS regression problem. Lasso simply does not belong to that kind of problem, which is why "the matrix being i |
43,351 | If linear combination of two time series processes is non-stationary does it mean one of the two series is non-stationary | Here's a simple counterexample (for discrete time). Let $X_t$ and $Z_t$ be iid standard Normal sequences. Let $\alpha_t$ be a sequence of numbers in $(-1,1)$. Define $Y_t=\alpha_t X_t+\beta_t Z_t$. Now
$Y_t$ is independent for different times.
The variance of $Y_t$ is $\alpha_t^2+\beta_t^2$ so given any $\alpha_t$ ... | If linear combination of two time series processes is non-stationary does it mean one of the two ser | Here's a simple counterexample (for discrete time). Let $X_t$ and $Z_t$ be iid standard Normal sequences. Let $\alpha_t$ be a sequence of numbers in $(-1,1)$. Define $Y_t=\alpha_t X_t+\beta_t Z_t$. | If linear combination of two time series processes is non-stationary does it mean one of the two series is non-stationary
Here's a simple counterexample (for discrete time). Let $X_t$ and $Z_t$ be iid standard Normal sequences. Let $\alpha_t$ be a sequence of numbers in $(-1,1)$. Define $Y_t=\alpha_t X_t+\beta_t Z_t$... | If linear combination of two time series processes is non-stationary does it mean one of the two ser
Here's a simple counterexample (for discrete time). Let $X_t$ and $Z_t$ be iid standard Normal sequences. Let $\alpha_t$ be a sequence of numbers in $(-1,1)$. Define $Y_t=\alpha_t X_t+\beta_t Z_t$. |
43,352 | If linear combination of two time series processes is non-stationary does it mean one of the two series is non-stationary | Let $$Z_t=aX_t+bY_t$$
Given that $Z_t$ is is NOT stationary, but $X_t$ and $Y_t$ are stationary.
So, we can say that $\exists \,\ s \neq t$ such that $$E(Z_t)\neq E(Z_s)$$
$$\implies aE(X_t) + bE(Y_t) \neq aE(X_s) + bE(Y_s)$$
However, $E(X_t)= E(X_s)$ and $E(Y_t)= E(Y_s)$, $\forall \,\, t,s$
So there is a contradiction... | If linear combination of two time series processes is non-stationary does it mean one of the two ser | Let $$Z_t=aX_t+bY_t$$
Given that $Z_t$ is is NOT stationary, but $X_t$ and $Y_t$ are stationary.
So, we can say that $\exists \,\ s \neq t$ such that $$E(Z_t)\neq E(Z_s)$$
$$\implies aE(X_t) + bE(Y_t) | If linear combination of two time series processes is non-stationary does it mean one of the two series is non-stationary
Let $$Z_t=aX_t+bY_t$$
Given that $Z_t$ is is NOT stationary, but $X_t$ and $Y_t$ are stationary.
So, we can say that $\exists \,\ s \neq t$ such that $$E(Z_t)\neq E(Z_s)$$
$$\implies aE(X_t) + bE(Y_... | If linear combination of two time series processes is non-stationary does it mean one of the two ser
Let $$Z_t=aX_t+bY_t$$
Given that $Z_t$ is is NOT stationary, but $X_t$ and $Y_t$ are stationary.
So, we can say that $\exists \,\ s \neq t$ such that $$E(Z_t)\neq E(Z_s)$$
$$\implies aE(X_t) + bE(Y_t) |
43,353 | How is TD(1) of TD(lambda) equivalent to Monte Carlo? | The last equation in your description is valid as long as it's episodic. It's not necessary to ask for it from the first equation. So if you directly write it with the natural meaning, then it's OK if lambda is 1.
By "natural meaning": the lambda return of TD(lambda) is the weighted average of future returns in each st... | How is TD(1) of TD(lambda) equivalent to Monte Carlo? | The last equation in your description is valid as long as it's episodic. It's not necessary to ask for it from the first equation. So if you directly write it with the natural meaning, then it's OK if | How is TD(1) of TD(lambda) equivalent to Monte Carlo?
The last equation in your description is valid as long as it's episodic. It's not necessary to ask for it from the first equation. So if you directly write it with the natural meaning, then it's OK if lambda is 1.
By "natural meaning": the lambda return of TD(lambda... | How is TD(1) of TD(lambda) equivalent to Monte Carlo?
The last equation in your description is valid as long as it's episodic. It's not necessary to ask for it from the first equation. So if you directly write it with the natural meaning, then it's OK if |
43,354 | Bounds on $P(Y, X)$ with $P(Y)$ and $P(X)$ known, as well as $X \geq Y$ | Update: A sharp lower bound is given in Corollary 2.4 in Nutz, Marcel, and Ruodu Wang. "The Directional Optimal Transport." arXiv preprint arXiv:2002.08717 (2020).
A bound exploiting the inequality constraint is given by
Smith, Woollcott. "Inequalities for bivariate distribution with x ≤ y
and marginals given." Com... | Bounds on $P(Y, X)$ with $P(Y)$ and $P(X)$ known, as well as $X \geq Y$ | Update: A sharp lower bound is given in Corollary 2.4 in Nutz, Marcel, and Ruodu Wang. "The Directional Optimal Transport." arXiv preprint arXiv:2002.08717 (2020).
A bound exploiting the inequality co | Bounds on $P(Y, X)$ with $P(Y)$ and $P(X)$ known, as well as $X \geq Y$
Update: A sharp lower bound is given in Corollary 2.4 in Nutz, Marcel, and Ruodu Wang. "The Directional Optimal Transport." arXiv preprint arXiv:2002.08717 (2020).
A bound exploiting the inequality constraint is given by
Smith, Woollcott. "Inequa... | Bounds on $P(Y, X)$ with $P(Y)$ and $P(X)$ known, as well as $X \geq Y$
Update: A sharp lower bound is given in Corollary 2.4 in Nutz, Marcel, and Ruodu Wang. "The Directional Optimal Transport." arXiv preprint arXiv:2002.08717 (2020).
A bound exploiting the inequality co |
43,355 | Justification for and optimality of $R^2_{adj.}$ as a model selection criterion | I don't know if $R^2_{\text{adj.}}$ have any optimal properties for model selection, but it is surely taught (or at least mentioned) in that context. One reason might be because most students have met $R^2$ early on, so there is then something to build on.
One example is the following exam paper from University of Osl... | Justification for and optimality of $R^2_{adj.}$ as a model selection criterion | I don't know if $R^2_{\text{adj.}}$ have any optimal properties for model selection, but it is surely taught (or at least mentioned) in that context. One reason might be because most students have met | Justification for and optimality of $R^2_{adj.}$ as a model selection criterion
I don't know if $R^2_{\text{adj.}}$ have any optimal properties for model selection, but it is surely taught (or at least mentioned) in that context. One reason might be because most students have met $R^2$ early on, so there is then someth... | Justification for and optimality of $R^2_{adj.}$ as a model selection criterion
I don't know if $R^2_{\text{adj.}}$ have any optimal properties for model selection, but it is surely taught (or at least mentioned) in that context. One reason might be because most students have met |
43,356 | Justification for and optimality of $R^2_{adj.}$ as a model selection criterion | Answer for part1:
If you add more variables, even totally insignificant variable, R2 can only go up. this is not the case with adjusted R2.
You can try running multiple regression and then add random variable and see what happened to R2 and what happened to the adjusted R2. | Justification for and optimality of $R^2_{adj.}$ as a model selection criterion | Answer for part1:
If you add more variables, even totally insignificant variable, R2 can only go up. this is not the case with adjusted R2.
You can try running multiple regression and then add random | Justification for and optimality of $R^2_{adj.}$ as a model selection criterion
Answer for part1:
If you add more variables, even totally insignificant variable, R2 can only go up. this is not the case with adjusted R2.
You can try running multiple regression and then add random variable and see what happened to R2 an... | Justification for and optimality of $R^2_{adj.}$ as a model selection criterion
Answer for part1:
If you add more variables, even totally insignificant variable, R2 can only go up. this is not the case with adjusted R2.
You can try running multiple regression and then add random |
43,357 | Justification for and optimality of $R^2_{adj.}$ as a model selection criterion | I would propose six optimality properties.
Overfit Mitigation
Simplicity and Parsimony
General Shared Understanding
Semi-Efficient Factor Identification
Robustness to Sample Size Change
Explanatory Utility
Overfit Mitigation
What kind of model is overfit? In part, this depends on the model's use case. Suppose we are ... | Justification for and optimality of $R^2_{adj.}$ as a model selection criterion | I would propose six optimality properties.
Overfit Mitigation
Simplicity and Parsimony
General Shared Understanding
Semi-Efficient Factor Identification
Robustness to Sample Size Change
Explanatory U | Justification for and optimality of $R^2_{adj.}$ as a model selection criterion
I would propose six optimality properties.
Overfit Mitigation
Simplicity and Parsimony
General Shared Understanding
Semi-Efficient Factor Identification
Robustness to Sample Size Change
Explanatory Utility
Overfit Mitigation
What kind of ... | Justification for and optimality of $R^2_{adj.}$ as a model selection criterion
I would propose six optimality properties.
Overfit Mitigation
Simplicity and Parsimony
General Shared Understanding
Semi-Efficient Factor Identification
Robustness to Sample Size Change
Explanatory U |
43,358 | distribution for scaled Maximum of n independent Weibulls for $n \to \infty$ | The determination of the domain of attraction and of the related
constants $a_n$ and $b_n$ uses several functions related to the survival
function, here given by $S(x) = \exp\{-(\lambda x)^k\}$ for $x > 0$.
Of major importance are the tail-quantile function $U(t)$ and the
hazard rate function $h(x)$.
The tail-quantile ... | distribution for scaled Maximum of n independent Weibulls for $n \to \infty$ | The determination of the domain of attraction and of the related
constants $a_n$ and $b_n$ uses several functions related to the survival
function, here given by $S(x) = \exp\{-(\lambda x)^k\}$ for $x | distribution for scaled Maximum of n independent Weibulls for $n \to \infty$
The determination of the domain of attraction and of the related
constants $a_n$ and $b_n$ uses several functions related to the survival
function, here given by $S(x) = \exp\{-(\lambda x)^k\}$ for $x > 0$.
Of major importance are the tail-qua... | distribution for scaled Maximum of n independent Weibulls for $n \to \infty$
The determination of the domain of attraction and of the related
constants $a_n$ and $b_n$ uses several functions related to the survival
function, here given by $S(x) = \exp\{-(\lambda x)^k\}$ for $x |
43,359 | "Return values" of univariate logistic regression | Recall that logistic regression is a model that predicts the odds
$$
\frac{p(x)}{1 - p(x)} = e^{\beta_0 + \beta_1 x}
$$
Now look at the Preposition 1 in the paper (p 5062)
$$
LR_\text{logistic}(\ln \tfrac{s}{1-s};a,c) = \exp[ a\ln\tfrac{s}{1-s}+c ]
$$
It seems like Algorithm 1 (p 5063) is a "univariate logistic regress... | "Return values" of univariate logistic regression | Recall that logistic regression is a model that predicts the odds
$$
\frac{p(x)}{1 - p(x)} = e^{\beta_0 + \beta_1 x}
$$
Now look at the Preposition 1 in the paper (p 5062)
$$
LR_\text{logistic}(\ln \t | "Return values" of univariate logistic regression
Recall that logistic regression is a model that predicts the odds
$$
\frac{p(x)}{1 - p(x)} = e^{\beta_0 + \beta_1 x}
$$
Now look at the Preposition 1 in the paper (p 5062)
$$
LR_\text{logistic}(\ln \tfrac{s}{1-s};a,c) = \exp[ a\ln\tfrac{s}{1-s}+c ]
$$
It seems like Algo... | "Return values" of univariate logistic regression
Recall that logistic regression is a model that predicts the odds
$$
\frac{p(x)}{1 - p(x)} = e^{\beta_0 + \beta_1 x}
$$
Now look at the Preposition 1 in the paper (p 5062)
$$
LR_\text{logistic}(\ln \t |
43,360 | Does having the same order statistics imply the same distribution? | The distribution of $(Y_1,\ldots,Y_n)$ is identified by the joint distribution of the order statistic $(Y_{(1)},\ldots,Y_{(n)})$ and of the rank statistic $(\sigma_1,\ldots,\sigma_n)$. In the case of an independent sample, the later distribution is uniform over the set of all possible permutations. But it could be any ... | Does having the same order statistics imply the same distribution? | The distribution of $(Y_1,\ldots,Y_n)$ is identified by the joint distribution of the order statistic $(Y_{(1)},\ldots,Y_{(n)})$ and of the rank statistic $(\sigma_1,\ldots,\sigma_n)$. In the case of | Does having the same order statistics imply the same distribution?
The distribution of $(Y_1,\ldots,Y_n)$ is identified by the joint distribution of the order statistic $(Y_{(1)},\ldots,Y_{(n)})$ and of the rank statistic $(\sigma_1,\ldots,\sigma_n)$. In the case of an independent sample, the later distribution is unif... | Does having the same order statistics imply the same distribution?
The distribution of $(Y_1,\ldots,Y_n)$ is identified by the joint distribution of the order statistic $(Y_{(1)},\ldots,Y_{(n)})$ and of the rank statistic $(\sigma_1,\ldots,\sigma_n)$. In the case of |
43,361 | Efficient Estimator from Insufficient Statistic | Since [under assumptions of its existence] a minimal sufficient statistic $S_n$ is a function of a sample $(X_1,\ldots,X_n)$,$$S_n=S_n(X_1,\ldots,X_n)$$
an efficient estimator $\hat{\theta}(S)$ can be written as$$\hat{\theta}(S(X_1,\ldots,X_n))$$which makes the question difficult to understand.
Note that the Cramèr-Rao... | Efficient Estimator from Insufficient Statistic | Since [under assumptions of its existence] a minimal sufficient statistic $S_n$ is a function of a sample $(X_1,\ldots,X_n)$,$$S_n=S_n(X_1,\ldots,X_n)$$
an efficient estimator $\hat{\theta}(S)$ can be | Efficient Estimator from Insufficient Statistic
Since [under assumptions of its existence] a minimal sufficient statistic $S_n$ is a function of a sample $(X_1,\ldots,X_n)$,$$S_n=S_n(X_1,\ldots,X_n)$$
an efficient estimator $\hat{\theta}(S)$ can be written as$$\hat{\theta}(S(X_1,\ldots,X_n))$$which makes the question d... | Efficient Estimator from Insufficient Statistic
Since [under assumptions of its existence] a minimal sufficient statistic $S_n$ is a function of a sample $(X_1,\ldots,X_n)$,$$S_n=S_n(X_1,\ldots,X_n)$$
an efficient estimator $\hat{\theta}(S)$ can be |
43,362 | Extreme Value Theory - domains of attraction and techniques for evaluting a limit | Preliminary interpretation: Your question does not clearly specify what you mean by $w(G)$, but you say that this is an "upper end point"of the distribution. I am going to assume that you mean that $t \uparrow w(G)$ implies $G(t) \uparrow 1$. My answer proceeds on this basis.
To simplify the notation in this problem... | Extreme Value Theory - domains of attraction and techniques for evaluting a limit | Preliminary interpretation: Your question does not clearly specify what you mean by $w(G)$, but you say that this is an "upper end point"of the distribution. I am going to assume that you mean that $ | Extreme Value Theory - domains of attraction and techniques for evaluting a limit
Preliminary interpretation: Your question does not clearly specify what you mean by $w(G)$, but you say that this is an "upper end point"of the distribution. I am going to assume that you mean that $t \uparrow w(G)$ implies $G(t) \uparro... | Extreme Value Theory - domains of attraction and techniques for evaluting a limit
Preliminary interpretation: Your question does not clearly specify what you mean by $w(G)$, but you say that this is an "upper end point"of the distribution. I am going to assume that you mean that $ |
43,363 | In linear regression, data is highly skewed, transformation doesn't work..! | There are too many questions asked. You are welcome to break it down. And many of the questions are already answered well in this forum.
I will only address your first question here.
There's more variables, and most of them are heavily skewed. (mostly right or some left)
It is seems you may have some mis-understandin... | In linear regression, data is highly skewed, transformation doesn't work..! | There are too many questions asked. You are welcome to break it down. And many of the questions are already answered well in this forum.
I will only address your first question here.
There's more var | In linear regression, data is highly skewed, transformation doesn't work..!
There are too many questions asked. You are welcome to break it down. And many of the questions are already answered well in this forum.
I will only address your first question here.
There's more variables, and most of them are heavily skewed.... | In linear regression, data is highly skewed, transformation doesn't work..!
There are too many questions asked. You are welcome to break it down. And many of the questions are already answered well in this forum.
I will only address your first question here.
There's more var |
43,364 | Predictive Posterior Distribution of Normal Distribution with Unknown Mean and Variance | If
$$p(\mu,\tau|\mathbf{x})\propto \tau^{\frac{n}{2}+ξ_{0}-1}\exp(-\tauξ_{0})\exp\left\{-\frac{1}{2}\tau\sum_{i=1}^{n}(x_{i}-\mu)^2-\frac{1}{2\sigma_{0}^{2}}(\mu-\mu_{0})^2\right\}\tag{1}$$
and (assuming $\tilde X\sim\mathcal N(\mu,\tau^{-1})$)
$$p(\tilde{x}|\mu,\tau)\propto \tau^{1/2}{\sqrt{2\pi}}\exp\{-\frac{1}{2}\ta... | Predictive Posterior Distribution of Normal Distribution with Unknown Mean and Variance | If
$$p(\mu,\tau|\mathbf{x})\propto \tau^{\frac{n}{2}+ξ_{0}-1}\exp(-\tauξ_{0})\exp\left\{-\frac{1}{2}\tau\sum_{i=1}^{n}(x_{i}-\mu)^2-\frac{1}{2\sigma_{0}^{2}}(\mu-\mu_{0})^2\right\}\tag{1}$$
and (assum | Predictive Posterior Distribution of Normal Distribution with Unknown Mean and Variance
If
$$p(\mu,\tau|\mathbf{x})\propto \tau^{\frac{n}{2}+ξ_{0}-1}\exp(-\tauξ_{0})\exp\left\{-\frac{1}{2}\tau\sum_{i=1}^{n}(x_{i}-\mu)^2-\frac{1}{2\sigma_{0}^{2}}(\mu-\mu_{0})^2\right\}\tag{1}$$
and (assuming $\tilde X\sim\mathcal N(\mu,... | Predictive Posterior Distribution of Normal Distribution with Unknown Mean and Variance
If
$$p(\mu,\tau|\mathbf{x})\propto \tau^{\frac{n}{2}+ξ_{0}-1}\exp(-\tauξ_{0})\exp\left\{-\frac{1}{2}\tau\sum_{i=1}^{n}(x_{i}-\mu)^2-\frac{1}{2\sigma_{0}^{2}}(\mu-\mu_{0})^2\right\}\tag{1}$$
and (assum |
43,365 | What is the difference between Silhouette Index vs Dunn Index vs Davies-Bouldin Index | Bolshakova and Azuaje (2003) presents an evaluation between the three cluster validation techniques you mention. Maybe this helps if you haven't seen it. | What is the difference between Silhouette Index vs Dunn Index vs Davies-Bouldin Index | Bolshakova and Azuaje (2003) presents an evaluation between the three cluster validation techniques you mention. Maybe this helps if you haven't seen it. | What is the difference between Silhouette Index vs Dunn Index vs Davies-Bouldin Index
Bolshakova and Azuaje (2003) presents an evaluation between the three cluster validation techniques you mention. Maybe this helps if you haven't seen it. | What is the difference between Silhouette Index vs Dunn Index vs Davies-Bouldin Index
Bolshakova and Azuaje (2003) presents an evaluation between the three cluster validation techniques you mention. Maybe this helps if you haven't seen it. |
43,366 | Fisher information for MLE with constraint | Constraints greatly complicate the usage of Fisher's information with asymptotic theory for derivation of an estimator's distribution. Two general tips:
1.) If you are dealing with equality constraints (which seems like the case in OP's problem), you can often use a change of parameterizations such that the parameter l... | Fisher information for MLE with constraint | Constraints greatly complicate the usage of Fisher's information with asymptotic theory for derivation of an estimator's distribution. Two general tips:
1.) If you are dealing with equality constraint | Fisher information for MLE with constraint
Constraints greatly complicate the usage of Fisher's information with asymptotic theory for derivation of an estimator's distribution. Two general tips:
1.) If you are dealing with equality constraints (which seems like the case in OP's problem), you can often use a change of ... | Fisher information for MLE with constraint
Constraints greatly complicate the usage of Fisher's information with asymptotic theory for derivation of an estimator's distribution. Two general tips:
1.) If you are dealing with equality constraint |
43,367 | Fisher information for MLE with constraint | I think not. The Fisher Information is a function of $\theta$, so it specifies what the what kind of performance you can expected of your estimator given a value of $\theta$. In some cases the FI depends on $\theta$, in some cases it does not. I don't think having a constraint on $\theta$ changes that.
What I would re... | Fisher information for MLE with constraint | I think not. The Fisher Information is a function of $\theta$, so it specifies what the what kind of performance you can expected of your estimator given a value of $\theta$. In some cases the FI depe | Fisher information for MLE with constraint
I think not. The Fisher Information is a function of $\theta$, so it specifies what the what kind of performance you can expected of your estimator given a value of $\theta$. In some cases the FI depends on $\theta$, in some cases it does not. I don't think having a constraint... | Fisher information for MLE with constraint
I think not. The Fisher Information is a function of $\theta$, so it specifies what the what kind of performance you can expected of your estimator given a value of $\theta$. In some cases the FI depe |
43,368 | Weight normalization technique used in Image Style Transfer | Short answer: Take the activation map corresponding to a particular weight matrix, take the mean of all the activations, and then average this mean over all images. Then divide the weight matrix and the bias by this average. And yes it makes sense to do it sequentially.
Long answer: (Using the notation used in the pape... | Weight normalization technique used in Image Style Transfer | Short answer: Take the activation map corresponding to a particular weight matrix, take the mean of all the activations, and then average this mean over all images. Then divide the weight matrix and t | Weight normalization technique used in Image Style Transfer
Short answer: Take the activation map corresponding to a particular weight matrix, take the mean of all the activations, and then average this mean over all images. Then divide the weight matrix and the bias by this average. And yes it makes sense to do it seq... | Weight normalization technique used in Image Style Transfer
Short answer: Take the activation map corresponding to a particular weight matrix, take the mean of all the activations, and then average this mean over all images. Then divide the weight matrix and t |
43,369 | Weight normalization technique used in Image Style Transfer | You are correct that once we have the mean feature activations over a set of images, we normalize the network sequentially, layer by layer. There is a subtlety involved, though. You can't rescale layer weights independently of the previous layers.
Let $W_i^l$ and $b_i^l$ be the weights and bias of the $i$-th convolutio... | Weight normalization technique used in Image Style Transfer | You are correct that once we have the mean feature activations over a set of images, we normalize the network sequentially, layer by layer. There is a subtlety involved, though. You can't rescale laye | Weight normalization technique used in Image Style Transfer
You are correct that once we have the mean feature activations over a set of images, we normalize the network sequentially, layer by layer. There is a subtlety involved, though. You can't rescale layer weights independently of the previous layers.
Let $W_i^l$ ... | Weight normalization technique used in Image Style Transfer
You are correct that once we have the mean feature activations over a set of images, we normalize the network sequentially, layer by layer. There is a subtlety involved, though. You can't rescale laye |
43,370 | how to sample data for regression that is the most informative? | I think this can be answered by drawing from sequential Monte-Carlo, and quasi Monte-Carlo methods.
The key concept that underlies your problem is the exploration-exploitation dilemma:
Exploration: you want to cover as much as possible of the space where $f$ is defined;
Exploitation: but ideally you would rather spe... | how to sample data for regression that is the most informative? | I think this can be answered by drawing from sequential Monte-Carlo, and quasi Monte-Carlo methods.
The key concept that underlies your problem is the exploration-exploitation dilemma:
Exploration: | how to sample data for regression that is the most informative?
I think this can be answered by drawing from sequential Monte-Carlo, and quasi Monte-Carlo methods.
The key concept that underlies your problem is the exploration-exploitation dilemma:
Exploration: you want to cover as much as possible of the space wher... | how to sample data for regression that is the most informative?
I think this can be answered by drawing from sequential Monte-Carlo, and quasi Monte-Carlo methods.
The key concept that underlies your problem is the exploration-exploitation dilemma:
Exploration: |
43,371 | Using ML to assist human labelling in dataset with highly unbalanced classes | I think the key is to keep in mind what you're really after. Is this a kaggle competition? Then sure, your approach sounds fine.
If this is for an academic paper, or medical work that will be put in the field, and you want something that will generalize well and pass peer review, then I don't think this is a good appro... | Using ML to assist human labelling in dataset with highly unbalanced classes | I think the key is to keep in mind what you're really after. Is this a kaggle competition? Then sure, your approach sounds fine.
If this is for an academic paper, or medical work that will be put in t | Using ML to assist human labelling in dataset with highly unbalanced classes
I think the key is to keep in mind what you're really after. Is this a kaggle competition? Then sure, your approach sounds fine.
If this is for an academic paper, or medical work that will be put in the field, and you want something that will ... | Using ML to assist human labelling in dataset with highly unbalanced classes
I think the key is to keep in mind what you're really after. Is this a kaggle competition? Then sure, your approach sounds fine.
If this is for an academic paper, or medical work that will be put in t |
43,372 | Using ML to assist human labelling in dataset with highly unbalanced classes | If there is a model that can label your data for you, then why even train one?
I would say using another model to label data for a model is bad. First, if a better model exists that can label a dataset why not just use that good model instead. Second, if the classes are highly unbalanced that is much more the reason t... | Using ML to assist human labelling in dataset with highly unbalanced classes | If there is a model that can label your data for you, then why even train one?
I would say using another model to label data for a model is bad. First, if a better model exists that can label a datas | Using ML to assist human labelling in dataset with highly unbalanced classes
If there is a model that can label your data for you, then why even train one?
I would say using another model to label data for a model is bad. First, if a better model exists that can label a dataset why not just use that good model instead... | Using ML to assist human labelling in dataset with highly unbalanced classes
If there is a model that can label your data for you, then why even train one?
I would say using another model to label data for a model is bad. First, if a better model exists that can label a datas |
43,373 | Using ML to assist human labelling in dataset with highly unbalanced classes | It is true that usually more data lead in better decisions. In your case, what you are trying to do is accelerate the labeling process and the way you propose to do it is valid. Since the question is which examples one should manually label and it is true that using a system to discard "uninteresting" examples with hig... | Using ML to assist human labelling in dataset with highly unbalanced classes | It is true that usually more data lead in better decisions. In your case, what you are trying to do is accelerate the labeling process and the way you propose to do it is valid. Since the question is | Using ML to assist human labelling in dataset with highly unbalanced classes
It is true that usually more data lead in better decisions. In your case, what you are trying to do is accelerate the labeling process and the way you propose to do it is valid. Since the question is which examples one should manually label an... | Using ML to assist human labelling in dataset with highly unbalanced classes
It is true that usually more data lead in better decisions. In your case, what you are trying to do is accelerate the labeling process and the way you propose to do it is valid. Since the question is |
43,374 | Ridge Regression and Lasso Regression | Does this indicate improvement of my predictor reduction from ridge to lasso?
No, the plots don't say anything about predictive performance. If you want to estimate that, you can use cross validation.
A.K.A, 6 predictors model does a better job in fitting the data than 8 predictors model?
Compared to ordinary least ... | Ridge Regression and Lasso Regression | Does this indicate improvement of my predictor reduction from ridge to lasso?
No, the plots don't say anything about predictive performance. If you want to estimate that, you can use cross validation | Ridge Regression and Lasso Regression
Does this indicate improvement of my predictor reduction from ridge to lasso?
No, the plots don't say anything about predictive performance. If you want to estimate that, you can use cross validation.
A.K.A, 6 predictors model does a better job in fitting the data than 8 predicto... | Ridge Regression and Lasso Regression
Does this indicate improvement of my predictor reduction from ridge to lasso?
No, the plots don't say anything about predictive performance. If you want to estimate that, you can use cross validation |
43,375 | Estimating weight of hidden coin | Method 1: Comparing double and single coin flips as two binomial distributed variables (biased)
Say you flip $n$ times both with $k_n$ times double heads, and you flip $m$ times the first with $k_m$ times the heads. The unknown weights $\theta_1$ and $\theta_2$ can be estimated by the maximum likelihood estimate:
The ... | Estimating weight of hidden coin | Method 1: Comparing double and single coin flips as two binomial distributed variables (biased)
Say you flip $n$ times both with $k_n$ times double heads, and you flip $m$ times the first with $k_m$ t | Estimating weight of hidden coin
Method 1: Comparing double and single coin flips as two binomial distributed variables (biased)
Say you flip $n$ times both with $k_n$ times double heads, and you flip $m$ times the first with $k_m$ times the heads. The unknown weights $\theta_1$ and $\theta_2$ can be estimated by the m... | Estimating weight of hidden coin
Method 1: Comparing double and single coin flips as two binomial distributed variables (biased)
Say you flip $n$ times both with $k_n$ times double heads, and you flip $m$ times the first with $k_m$ t |
43,376 | Meta analysis of Multiple regression | Meta-analysis of regression slopes is less common but not unheard of (for methodological details, see Aloe & Becker, 2012; Peterson & Brown, 2005). The typical concern with this approach is that it's often improbable (if not impossible) to ensure model equivalency about samples--the meaning of a slope will change if di... | Meta analysis of Multiple regression | Meta-analysis of regression slopes is less common but not unheard of (for methodological details, see Aloe & Becker, 2012; Peterson & Brown, 2005). The typical concern with this approach is that it's | Meta analysis of Multiple regression
Meta-analysis of regression slopes is less common but not unheard of (for methodological details, see Aloe & Becker, 2012; Peterson & Brown, 2005). The typical concern with this approach is that it's often improbable (if not impossible) to ensure model equivalency about samples--the... | Meta analysis of Multiple regression
Meta-analysis of regression slopes is less common but not unheard of (for methodological details, see Aloe & Becker, 2012; Peterson & Brown, 2005). The typical concern with this approach is that it's |
43,377 | Meta analysis of Multiple regression | Vaitsiakhovich, T., Drichel, D., Herold, C., Lacour, A., & Becker, T. (2015). METAINTER: meta-analysis of multiple regression models in genome-wide association studies. Bioinformatics, 31(2), 151-157.
This paper provides an introduction to software to carry out meta-analysis of regression models. It's made for bioinfo... | Meta analysis of Multiple regression | Vaitsiakhovich, T., Drichel, D., Herold, C., Lacour, A., & Becker, T. (2015). METAINTER: meta-analysis of multiple regression models in genome-wide association studies. Bioinformatics, 31(2), 151-157. | Meta analysis of Multiple regression
Vaitsiakhovich, T., Drichel, D., Herold, C., Lacour, A., & Becker, T. (2015). METAINTER: meta-analysis of multiple regression models in genome-wide association studies. Bioinformatics, 31(2), 151-157.
This paper provides an introduction to software to carry out meta-analysis of reg... | Meta analysis of Multiple regression
Vaitsiakhovich, T., Drichel, D., Herold, C., Lacour, A., & Becker, T. (2015). METAINTER: meta-analysis of multiple regression models in genome-wide association studies. Bioinformatics, 31(2), 151-157. |
43,378 | Meta analysis of Multiple regression | If I understood right, there are 3 regression models (of the same study) and you wish to use the results in a meta-analysis.
To start, if we consider we have, say, 3 "studies", I gather at least 4 studies would be necessary to get minimally reasonable results in a meta-analysis. That being said, if the results differ a... | Meta analysis of Multiple regression | If I understood right, there are 3 regression models (of the same study) and you wish to use the results in a meta-analysis.
To start, if we consider we have, say, 3 "studies", I gather at least 4 stu | Meta analysis of Multiple regression
If I understood right, there are 3 regression models (of the same study) and you wish to use the results in a meta-analysis.
To start, if we consider we have, say, 3 "studies", I gather at least 4 studies would be necessary to get minimally reasonable results in a meta-analysis. Tha... | Meta analysis of Multiple regression
If I understood right, there are 3 regression models (of the same study) and you wish to use the results in a meta-analysis.
To start, if we consider we have, say, 3 "studies", I gather at least 4 stu |
43,379 | GAM : smoothing splines | Question 1:
Why would you not want to use thin plate splines?
It is computationally costly to set up the basis functions for a thin plate spline. Typically you would need one basis function per (unique) data point; even though Simon's truncation process allows for far fewer basis functions to be used in fitting, you s... | GAM : smoothing splines | Question 1:
Why would you not want to use thin plate splines?
It is computationally costly to set up the basis functions for a thin plate spline. Typically you would need one basis function per (uniq | GAM : smoothing splines
Question 1:
Why would you not want to use thin plate splines?
It is computationally costly to set up the basis functions for a thin plate spline. Typically you would need one basis function per (unique) data point; even though Simon's truncation process allows for far fewer basis functions to b... | GAM : smoothing splines
Question 1:
Why would you not want to use thin plate splines?
It is computationally costly to set up the basis functions for a thin plate spline. Typically you would need one basis function per (uniq |
43,380 | Statistical test to identify enriched edges in a network | The type of modelling approach that you are describing looks a lot like what exponential random graph models (ERGM) are doing.
In essence, ERGMs are models that attempt to explain network structure. The idea is to understand the processes that lead to the existence of ties in an observed network. In your ERGM, you spe... | Statistical test to identify enriched edges in a network | The type of modelling approach that you are describing looks a lot like what exponential random graph models (ERGM) are doing.
In essence, ERGMs are models that attempt to explain network structure. | Statistical test to identify enriched edges in a network
The type of modelling approach that you are describing looks a lot like what exponential random graph models (ERGM) are doing.
In essence, ERGMs are models that attempt to explain network structure. The idea is to understand the processes that lead to the existe... | Statistical test to identify enriched edges in a network
The type of modelling approach that you are describing looks a lot like what exponential random graph models (ERGM) are doing.
In essence, ERGMs are models that attempt to explain network structure. |
43,381 | Help understanding a paragraph in Kadane's book Principles of uncertainty | There is usually some equation in the book relating to these variables: s,l,A can you add that information for clarity?
I believe that A not being an element of $s_i$ means that if you were to create a ven diagram A would not fall into the category of event indicators enclosed by $s_i$
$s^′_i$ seems to just be a nota... | Help understanding a paragraph in Kadane's book Principles of uncertainty | There is usually some equation in the book relating to these variables: s,l,A can you add that information for clarity?
I believe that A not being an element of $s_i$ means that if you were to create | Help understanding a paragraph in Kadane's book Principles of uncertainty
There is usually some equation in the book relating to these variables: s,l,A can you add that information for clarity?
I believe that A not being an element of $s_i$ means that if you were to create a ven diagram A would not fall into the categ... | Help understanding a paragraph in Kadane's book Principles of uncertainty
There is usually some equation in the book relating to these variables: s,l,A can you add that information for clarity?
I believe that A not being an element of $s_i$ means that if you were to create |
43,382 | Which is the Recommended variants of neural network for tree like data structures? | You might be in interested in, in contrast to a recurrent neural network, a recursive neural network, which
[applies] the same set of weights recursively over a structure, to produce a structured prediction over variable-size input structures, or a scalar prediction on it, by traversing a given structure in topologica... | Which is the Recommended variants of neural network for tree like data structures? | You might be in interested in, in contrast to a recurrent neural network, a recursive neural network, which
[applies] the same set of weights recursively over a structure, to produce a structured pre | Which is the Recommended variants of neural network for tree like data structures?
You might be in interested in, in contrast to a recurrent neural network, a recursive neural network, which
[applies] the same set of weights recursively over a structure, to produce a structured prediction over variable-size input stru... | Which is the Recommended variants of neural network for tree like data structures?
You might be in interested in, in contrast to a recurrent neural network, a recursive neural network, which
[applies] the same set of weights recursively over a structure, to produce a structured pre |
43,383 | Which is the Recommended variants of neural network for tree like data structures? | There is this paper on Neural Decision Trees. Where the decision tree is the network structure and each node is a perceptron. I also seem to call another paper by researchers in China that made headlines within the last 6 months but what I read about that one was that is was mostly hype. | Which is the Recommended variants of neural network for tree like data structures? | There is this paper on Neural Decision Trees. Where the decision tree is the network structure and each node is a perceptron. I also seem to call another paper by researchers in China that made headli | Which is the Recommended variants of neural network for tree like data structures?
There is this paper on Neural Decision Trees. Where the decision tree is the network structure and each node is a perceptron. I also seem to call another paper by researchers in China that made headlines within the last 6 months but what... | Which is the Recommended variants of neural network for tree like data structures?
There is this paper on Neural Decision Trees. Where the decision tree is the network structure and each node is a perceptron. I also seem to call another paper by researchers in China that made headli |
43,384 | Which adaptive Metropolis Hastings algorithm is implemented in R package MHadaptive? | Your description sounds like Haario et al (1999)'s adaptive algorithm. The idea there is indeed to update the covariance matrix of the proposal distribution using a fixed number of recent samples.
Note that the algorithm described in Haario et al (1999) performs well, but is NOT ergodic. Haario et al (2001) described a... | Which adaptive Metropolis Hastings algorithm is implemented in R package MHadaptive? | Your description sounds like Haario et al (1999)'s adaptive algorithm. The idea there is indeed to update the covariance matrix of the proposal distribution using a fixed number of recent samples.
Not | Which adaptive Metropolis Hastings algorithm is implemented in R package MHadaptive?
Your description sounds like Haario et al (1999)'s adaptive algorithm. The idea there is indeed to update the covariance matrix of the proposal distribution using a fixed number of recent samples.
Note that the algorithm described in H... | Which adaptive Metropolis Hastings algorithm is implemented in R package MHadaptive?
Your description sounds like Haario et al (1999)'s adaptive algorithm. The idea there is indeed to update the covariance matrix of the proposal distribution using a fixed number of recent samples.
Not |
43,385 | Unconditional distribution of ARMA process with t-student errors | The ARMA model is within the general class of linear models where your observable vector is a linear function of an underlying vector of IID error terms. Consider the general linear model form with IID errors following a T-distribution:
$$Y_t = \sum_{k=0}^\infty A_k \varepsilon_{t-k} \quad \quad \quad \varepsilon_k \s... | Unconditional distribution of ARMA process with t-student errors | The ARMA model is within the general class of linear models where your observable vector is a linear function of an underlying vector of IID error terms. Consider the general linear model form with I | Unconditional distribution of ARMA process with t-student errors
The ARMA model is within the general class of linear models where your observable vector is a linear function of an underlying vector of IID error terms. Consider the general linear model form with IID errors following a T-distribution:
$$Y_t = \sum_{k=0... | Unconditional distribution of ARMA process with t-student errors
The ARMA model is within the general class of linear models where your observable vector is a linear function of an underlying vector of IID error terms. Consider the general linear model form with I |
43,386 | Is this also a *necessary* condition to be a Bayes estimator, or only a sufficient one? | First, if the condition$$\delta_{\Lambda} = \arg\min \mathbb{E}\left[ L(\Theta, \delta(X))| X = x \right]$$ holds almost surely in $x$, the same argument applies. Hence, the Bayes estimator is defined almost surely and hence can arbitrarily vary on an arbitrary set of measure zero.
Second, there are situations where t... | Is this also a *necessary* condition to be a Bayes estimator, or only a sufficient one? | First, if the condition$$\delta_{\Lambda} = \arg\min \mathbb{E}\left[ L(\Theta, \delta(X))| X = x \right]$$ holds almost surely in $x$, the same argument applies. Hence, the Bayes estimator is define | Is this also a *necessary* condition to be a Bayes estimator, or only a sufficient one?
First, if the condition$$\delta_{\Lambda} = \arg\min \mathbb{E}\left[ L(\Theta, \delta(X))| X = x \right]$$ holds almost surely in $x$, the same argument applies. Hence, the Bayes estimator is defined almost surely and hence can ar... | Is this also a *necessary* condition to be a Bayes estimator, or only a sufficient one?
First, if the condition$$\delta_{\Lambda} = \arg\min \mathbb{E}\left[ L(\Theta, \delta(X))| X = x \right]$$ holds almost surely in $x$, the same argument applies. Hence, the Bayes estimator is define |
43,387 | How do bias, variance and overfitting relate to each other? [closed] | As for number 3:
Model with eta = 0.9 clearly shows overfitting, as the more it is trained, the more it is able to predict the training set and the more it is unable to predict the testing set. I can only think model with eta = 0.1 is underfitting because compared to other eta values, it performs the worse. Not only it... | How do bias, variance and overfitting relate to each other? [closed] | As for number 3:
Model with eta = 0.9 clearly shows overfitting, as the more it is trained, the more it is able to predict the training set and the more it is unable to predict the testing set. I can | How do bias, variance and overfitting relate to each other? [closed]
As for number 3:
Model with eta = 0.9 clearly shows overfitting, as the more it is trained, the more it is able to predict the training set and the more it is unable to predict the testing set. I can only think model with eta = 0.1 is underfitting bec... | How do bias, variance and overfitting relate to each other? [closed]
As for number 3:
Model with eta = 0.9 clearly shows overfitting, as the more it is trained, the more it is able to predict the training set and the more it is unable to predict the testing set. I can |
43,388 | How do bias, variance and overfitting relate to each other? [closed] | In ML we want to follow Occam's razor. Prefer low complexity over high complexity, because we assume that we will perform better on new unseen data, if we have a less complex model (less overfitting). But there is of course also underfitting! So if you can show that a more complex model can outperform a less complex mo... | How do bias, variance and overfitting relate to each other? [closed] | In ML we want to follow Occam's razor. Prefer low complexity over high complexity, because we assume that we will perform better on new unseen data, if we have a less complex model (less overfitting). | How do bias, variance and overfitting relate to each other? [closed]
In ML we want to follow Occam's razor. Prefer low complexity over high complexity, because we assume that we will perform better on new unseen data, if we have a less complex model (less overfitting). But there is of course also underfitting! So if yo... | How do bias, variance and overfitting relate to each other? [closed]
In ML we want to follow Occam's razor. Prefer low complexity over high complexity, because we assume that we will perform better on new unseen data, if we have a less complex model (less overfitting). |
43,389 | How do bias, variance and overfitting relate to each other? [closed] | As for number 2
You are correct in your intuition in that the two concerns you raise elude to slightly different things.
In your first instance, the recommendation to choose a simpler model to avoid over-fitting, refers to the over-fitting of the models parameters - the things a model learns based on the training data.... | How do bias, variance and overfitting relate to each other? [closed] | As for number 2
You are correct in your intuition in that the two concerns you raise elude to slightly different things.
In your first instance, the recommendation to choose a simpler model to avoid o | How do bias, variance and overfitting relate to each other? [closed]
As for number 2
You are correct in your intuition in that the two concerns you raise elude to slightly different things.
In your first instance, the recommendation to choose a simpler model to avoid over-fitting, refers to the over-fitting of the mode... | How do bias, variance and overfitting relate to each other? [closed]
As for number 2
You are correct in your intuition in that the two concerns you raise elude to slightly different things.
In your first instance, the recommendation to choose a simpler model to avoid o |
43,390 | Should random effects be included in fitted values when making a binned residual plot for a binomial GLMM? | In short, predictions should be made based on fixed effects only, otherwise you can get spurious patterns. This is highly reproducible and probably due to the regularisation bias of the REs.
Simulations to show this with some further comments here
p.s.: Have a look at the DHARMa residual checks for GLMMs, I think thi... | Should random effects be included in fitted values when making a binned residual plot for a binomial | In short, predictions should be made based on fixed effects only, otherwise you can get spurious patterns. This is highly reproducible and probably due to the regularisation bias of the REs.
Simulati | Should random effects be included in fitted values when making a binned residual plot for a binomial GLMM?
In short, predictions should be made based on fixed effects only, otherwise you can get spurious patterns. This is highly reproducible and probably due to the regularisation bias of the REs.
Simulations to show t... | Should random effects be included in fitted values when making a binned residual plot for a binomial
In short, predictions should be made based on fixed effects only, otherwise you can get spurious patterns. This is highly reproducible and probably due to the regularisation bias of the REs.
Simulati |
43,391 | Distribution of sampling without replacement | Assume the item weights $W = \{w(1), \dots, w(N)\}$ are nonnegative and sum to one. Let $X=\{x_1, \dots, x_N\}$ denote a particular sequence, where $x_i$ is an integer from 1 to $N$ representing the index of the $i$th item drawn. Let $X_{i:j} = \{x_i, \dots, x_j\}$ denote a particular subsequnce. The probability of a s... | Distribution of sampling without replacement | Assume the item weights $W = \{w(1), \dots, w(N)\}$ are nonnegative and sum to one. Let $X=\{x_1, \dots, x_N\}$ denote a particular sequence, where $x_i$ is an integer from 1 to $N$ representing the i | Distribution of sampling without replacement
Assume the item weights $W = \{w(1), \dots, w(N)\}$ are nonnegative and sum to one. Let $X=\{x_1, \dots, x_N\}$ denote a particular sequence, where $x_i$ is an integer from 1 to $N$ representing the index of the $i$th item drawn. Let $X_{i:j} = \{x_i, \dots, x_j\}$ denote a ... | Distribution of sampling without replacement
Assume the item weights $W = \{w(1), \dots, w(N)\}$ are nonnegative and sum to one. Let $X=\{x_1, \dots, x_N\}$ denote a particular sequence, where $x_i$ is an integer from 1 to $N$ representing the i |
43,392 | Distribution of sampling without replacement | I'm working on a similar problem. This is not a direct answer to your question but maybe it's close enough that it gives you some ideas about how to start.
My weights form an exponential (specifically, log-linear) family $w_i = \exp(\beta^\top a_i)/Z(\beta)$, where $Z(\beta) = 1 + \exp(\beta^\top a_i)$, $\beta$ is the ... | Distribution of sampling without replacement | I'm working on a similar problem. This is not a direct answer to your question but maybe it's close enough that it gives you some ideas about how to start.
My weights form an exponential (specifically | Distribution of sampling without replacement
I'm working on a similar problem. This is not a direct answer to your question but maybe it's close enough that it gives you some ideas about how to start.
My weights form an exponential (specifically, log-linear) family $w_i = \exp(\beta^\top a_i)/Z(\beta)$, where $Z(\beta)... | Distribution of sampling without replacement
I'm working on a similar problem. This is not a direct answer to your question but maybe it's close enough that it gives you some ideas about how to start.
My weights form an exponential (specifically |
43,393 | P-values and likelihood principle | Your intuition seems reasonable here, but it is worth stating things more precisely. So long as the p-value for the test is not a function of a sufficient statistic, the sufficiency principle is breached. For the conditionality principle, things are a bit trickier. The conditionality principle was originally describ... | P-values and likelihood principle | Your intuition seems reasonable here, but it is worth stating things more precisely. So long as the p-value for the test is not a function of a sufficient statistic, the sufficiency principle is brea | P-values and likelihood principle
Your intuition seems reasonable here, but it is worth stating things more precisely. So long as the p-value for the test is not a function of a sufficient statistic, the sufficiency principle is breached. For the conditionality principle, things are a bit trickier. The conditionalit... | P-values and likelihood principle
Your intuition seems reasonable here, but it is worth stating things more precisely. So long as the p-value for the test is not a function of a sufficient statistic, the sufficiency principle is brea |
43,394 | CNN + LSTM in tensorflow | CNN + RNN possible. To understand let me try to post commented code. CNN running of chars of sentences and output of CNN merged with word embedding is feed to LSTM
N - number of batches
M - number of examples
L - number of sentence length
W - max length of characters in any word
coz - cnn char output size
Consider ... | CNN + LSTM in tensorflow | CNN + RNN possible. To understand let me try to post commented code. CNN running of chars of sentences and output of CNN merged with word embedding is feed to LSTM
N - number of batches
M - number o | CNN + LSTM in tensorflow
CNN + RNN possible. To understand let me try to post commented code. CNN running of chars of sentences and output of CNN merged with word embedding is feed to LSTM
N - number of batches
M - number of examples
L - number of sentence length
W - max length of characters in any word
coz - cnn c... | CNN + LSTM in tensorflow
CNN + RNN possible. To understand let me try to post commented code. CNN running of chars of sentences and output of CNN merged with word embedding is feed to LSTM
N - number of batches
M - number o |
43,395 | CNN + LSTM in tensorflow | Check out https://github.com/NLeSC/mcfly, it helps to search and configure best architecture for deep cnn+lstm. You may check the source to learn. | CNN + LSTM in tensorflow | Check out https://github.com/NLeSC/mcfly, it helps to search and configure best architecture for deep cnn+lstm. You may check the source to learn. | CNN + LSTM in tensorflow
Check out https://github.com/NLeSC/mcfly, it helps to search and configure best architecture for deep cnn+lstm. You may check the source to learn. | CNN + LSTM in tensorflow
Check out https://github.com/NLeSC/mcfly, it helps to search and configure best architecture for deep cnn+lstm. You may check the source to learn. |
43,396 | What is the difference between Online Learning Algorithms and Streaming Learning Algorithms? | An example of an online algorithm application would be investing, where you have to reconsider the investment decision upon the arrival of new data. You cannot defer this decision and you cannot revise old decisions. I am not familiar with streaming algorithms, but it seems (based on your professor's slides) that data ... | What is the difference between Online Learning Algorithms and Streaming Learning Algorithms? | An example of an online algorithm application would be investing, where you have to reconsider the investment decision upon the arrival of new data. You cannot defer this decision and you cannot revis | What is the difference between Online Learning Algorithms and Streaming Learning Algorithms?
An example of an online algorithm application would be investing, where you have to reconsider the investment decision upon the arrival of new data. You cannot defer this decision and you cannot revise old decisions. I am not f... | What is the difference between Online Learning Algorithms and Streaming Learning Algorithms?
An example of an online algorithm application would be investing, where you have to reconsider the investment decision upon the arrival of new data. You cannot defer this decision and you cannot revis |
43,397 | Comparing two multinomial distributions | I believe the best test for your data is the multinomial test, which you already estimated.
For a "measure" of the difference between the expected and observed distributions, consider the Kullback-Leibler Divergence. For your first experiment:
require(entropy)
cv <- d[1:4,]
set.seed(42)
probs <- cv$freq_pred/20
cv$freq... | Comparing two multinomial distributions | I believe the best test for your data is the multinomial test, which you already estimated.
For a "measure" of the difference between the expected and observed distributions, consider the Kullback-Lei | Comparing two multinomial distributions
I believe the best test for your data is the multinomial test, which you already estimated.
For a "measure" of the difference between the expected and observed distributions, consider the Kullback-Leibler Divergence. For your first experiment:
require(entropy)
cv <- d[1:4,]
set.s... | Comparing two multinomial distributions
I believe the best test for your data is the multinomial test, which you already estimated.
For a "measure" of the difference between the expected and observed distributions, consider the Kullback-Lei |
43,398 | Expected squared distance from origin of training points vs. test points | The relevant quote from page 23 of Elements of Statistics: "Hence most data points are closer to the boundary
of the sample space than to any other data point. The reason that this
presents a problem is that prediction is much more difficult near the edges
of the training sample. One must extrapolate from neighboring s... | Expected squared distance from origin of training points vs. test points | The relevant quote from page 23 of Elements of Statistics: "Hence most data points are closer to the boundary
of the sample space than to any other data point. The reason that this
presents a problem | Expected squared distance from origin of training points vs. test points
The relevant quote from page 23 of Elements of Statistics: "Hence most data points are closer to the boundary
of the sample space than to any other data point. The reason that this
presents a problem is that prediction is much more difficult near ... | Expected squared distance from origin of training points vs. test points
The relevant quote from page 23 of Elements of Statistics: "Hence most data points are closer to the boundary
of the sample space than to any other data point. The reason that this
presents a problem |
43,399 | Expected squared distance from origin of training points vs. test points | For the question "Why we are taking the unit vector projection of one point onto all the training points", it is easy to figure it out because when you make a prediction, the neighbors are chosen from training data which are close to the prediction point $x_0$. And the unit vector projection is the distance of training... | Expected squared distance from origin of training points vs. test points | For the question "Why we are taking the unit vector projection of one point onto all the training points", it is easy to figure it out because when you make a prediction, the neighbors are chosen from | Expected squared distance from origin of training points vs. test points
For the question "Why we are taking the unit vector projection of one point onto all the training points", it is easy to figure it out because when you make a prediction, the neighbors are chosen from training data which are close to the predictio... | Expected squared distance from origin of training points vs. test points
For the question "Why we are taking the unit vector projection of one point onto all the training points", it is easy to figure it out because when you make a prediction, the neighbors are chosen from |
43,400 | Which binomial confidence interval is correct? | The Wilson score interval is a simple and accurate confidence interval for the binomial proportion parameter, that automatically adjusts near the boundaries of the range. The coverage properties of various intervals have been examined in Brown, Cai and DasGupta (2001) and this is one of the intervals they recommend as... | Which binomial confidence interval is correct? | The Wilson score interval is a simple and accurate confidence interval for the binomial proportion parameter, that automatically adjusts near the boundaries of the range. The coverage properties of v | Which binomial confidence interval is correct?
The Wilson score interval is a simple and accurate confidence interval for the binomial proportion parameter, that automatically adjusts near the boundaries of the range. The coverage properties of various intervals have been examined in Brown, Cai and DasGupta (2001) and... | Which binomial confidence interval is correct?
The Wilson score interval is a simple and accurate confidence interval for the binomial proportion parameter, that automatically adjusts near the boundaries of the range. The coverage properties of v |
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