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 |
|---|---|---|---|---|---|---|
9,301 | Statistical methods for data where only a minimum/maximum value is known | This is a case of censoring/coarse data. Assume you think that your data arises from a distribution with nicely behaved continuous (etc.) pdf $f(x)$ and cdf $F(x)$. The standard solution for time to event data when the exact time $x_i$ of an event for subject $i$ is known is that the likelihood contribution is $f(x_i)$... | Statistical methods for data where only a minimum/maximum value is known | This is a case of censoring/coarse data. Assume you think that your data arises from a distribution with nicely behaved continuous (etc.) pdf $f(x)$ and cdf $F(x)$. The standard solution for time to e | Statistical methods for data where only a minimum/maximum value is known
This is a case of censoring/coarse data. Assume you think that your data arises from a distribution with nicely behaved continuous (etc.) pdf $f(x)$ and cdf $F(x)$. The standard solution for time to event data when the exact time $x_i$ of an event... | Statistical methods for data where only a minimum/maximum value is known
This is a case of censoring/coarse data. Assume you think that your data arises from a distribution with nicely behaved continuous (etc.) pdf $f(x)$ and cdf $F(x)$. The standard solution for time to e |
9,302 | Statistical methods for data where only a minimum/maximum value is known | This problem seems like it might be handled well by logistic regression.
You have two states, A and B, and want to examine the probability of whether a particular individual has switched irreversibly from state A to state B. One fundamental predictor variable would be age at the time of observation. The other factor or... | Statistical methods for data where only a minimum/maximum value is known | This problem seems like it might be handled well by logistic regression.
You have two states, A and B, and want to examine the probability of whether a particular individual has switched irreversibly | Statistical methods for data where only a minimum/maximum value is known
This problem seems like it might be handled well by logistic regression.
You have two states, A and B, and want to examine the probability of whether a particular individual has switched irreversibly from state A to state B. One fundamental predic... | Statistical methods for data where only a minimum/maximum value is known
This problem seems like it might be handled well by logistic regression.
You have two states, A and B, and want to examine the probability of whether a particular individual has switched irreversibly |
9,303 | Why is glmnet ridge regression giving me a different answer than manual calculation? | The difference you are observing is due to the additional division by the number of observations, N, that GLMNET uses in their objective function and implicit standardization of Y by its sample standard deviation as shown below.
$$
\frac{1}{2N}\left\|\frac{y}{s_y}-X\beta\right\|^2_{2}+\lambda\|\beta\|^2_{2}/2
$$
where ... | Why is glmnet ridge regression giving me a different answer than manual calculation? | The difference you are observing is due to the additional division by the number of observations, N, that GLMNET uses in their objective function and implicit standardization of Y by its sample standa | Why is glmnet ridge regression giving me a different answer than manual calculation?
The difference you are observing is due to the additional division by the number of observations, N, that GLMNET uses in their objective function and implicit standardization of Y by its sample standard deviation as shown below.
$$
\fr... | Why is glmnet ridge regression giving me a different answer than manual calculation?
The difference you are observing is due to the additional division by the number of observations, N, that GLMNET uses in their objective function and implicit standardization of Y by its sample standa |
9,304 | Why is glmnet ridge regression giving me a different answer than manual calculation? | According to https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html, when the family is gaussian, glmnet() should minimize
$$\frac{1}{2n} \sum_{i=1}^n (y_i-\beta_0-x_i^T\beta)^2
+\lambda\sum_{j=1}^p(\alpha|\beta_j|
+(1-\alpha)\beta_j^2/2). \tag{1}$$
When using glmnet(x, y, alpha=1) to fit the lasso with the columns... | Why is glmnet ridge regression giving me a different answer than manual calculation? | According to https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html, when the family is gaussian, glmnet() should minimize
$$\frac{1}{2n} \sum_{i=1}^n (y_i-\beta_0-x_i^T\beta)^2
+\lambda\sum_{j=1} | Why is glmnet ridge regression giving me a different answer than manual calculation?
According to https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html, when the family is gaussian, glmnet() should minimize
$$\frac{1}{2n} \sum_{i=1}^n (y_i-\beta_0-x_i^T\beta)^2
+\lambda\sum_{j=1}^p(\alpha|\beta_j|
+(1-\alpha)\beta... | Why is glmnet ridge regression giving me a different answer than manual calculation?
According to https://web.stanford.edu/~hastie/glmnet/glmnet_alpha.html, when the family is gaussian, glmnet() should minimize
$$\frac{1}{2n} \sum_{i=1}^n (y_i-\beta_0-x_i^T\beta)^2
+\lambda\sum_{j=1} |
9,305 | What is the difference in what AIC and c-statistic (AUC) actually measure for model fit? | AIC and c-statistic are trying to answer different questions. (Also some issues with c-statistic have been raised in recent years, but I'll come onto that as an aside)
Roughly speaking:
AIC is telling you how good your model fits for a specific mis-classification cost.
AUC is telling you how good your model would wor... | What is the difference in what AIC and c-statistic (AUC) actually measure for model fit? | AIC and c-statistic are trying to answer different questions. (Also some issues with c-statistic have been raised in recent years, but I'll come onto that as an aside)
Roughly speaking:
AIC is telli | What is the difference in what AIC and c-statistic (AUC) actually measure for model fit?
AIC and c-statistic are trying to answer different questions. (Also some issues with c-statistic have been raised in recent years, but I'll come onto that as an aside)
Roughly speaking:
AIC is telling you how good your model fits... | What is the difference in what AIC and c-statistic (AUC) actually measure for model fit?
AIC and c-statistic are trying to answer different questions. (Also some issues with c-statistic have been raised in recent years, but I'll come onto that as an aside)
Roughly speaking:
AIC is telli |
9,306 | What is the difference in what AIC and c-statistic (AUC) actually measure for model fit? | The Hand paper cited has no basis in real world use in clinical diagnostics. He has a theoretical curve with a 0.5 AUC, which is instead a perfect classifier. He uses a single set of real-world data, where the models would be thrown away out of hand, as they are so bad, and when accounting for the confidence intervals ... | What is the difference in what AIC and c-statistic (AUC) actually measure for model fit? | The Hand paper cited has no basis in real world use in clinical diagnostics. He has a theoretical curve with a 0.5 AUC, which is instead a perfect classifier. He uses a single set of real-world data, | What is the difference in what AIC and c-statistic (AUC) actually measure for model fit?
The Hand paper cited has no basis in real world use in clinical diagnostics. He has a theoretical curve with a 0.5 AUC, which is instead a perfect classifier. He uses a single set of real-world data, where the models would be throw... | What is the difference in what AIC and c-statistic (AUC) actually measure for model fit?
The Hand paper cited has no basis in real world use in clinical diagnostics. He has a theoretical curve with a 0.5 AUC, which is instead a perfect classifier. He uses a single set of real-world data, |
9,307 | What is the difference in what AIC and c-statistic (AUC) actually measure for model fit? | For me, the bottom line is that while the C-statistic (AUC) may be problematic when comparing models with different independent variables (analogous to what Hand refers to as "classifiers"), it is still useful in other applications. For instance, validation studies where the same model is compared across different stud... | What is the difference in what AIC and c-statistic (AUC) actually measure for model fit? | For me, the bottom line is that while the C-statistic (AUC) may be problematic when comparing models with different independent variables (analogous to what Hand refers to as "classifiers"), it is sti | What is the difference in what AIC and c-statistic (AUC) actually measure for model fit?
For me, the bottom line is that while the C-statistic (AUC) may be problematic when comparing models with different independent variables (analogous to what Hand refers to as "classifiers"), it is still useful in other applications... | What is the difference in what AIC and c-statistic (AUC) actually measure for model fit?
For me, the bottom line is that while the C-statistic (AUC) may be problematic when comparing models with different independent variables (analogous to what Hand refers to as "classifiers"), it is sti |
9,308 | Supervised clustering or classification? | My naive understanding is that classification is performed where you have a specified set of classes and you want to classify a new thing/dataset into one of those specified classes.
Alternatively, clustering has nothing to start with and you use all the data (including the new one) to separate into clusters.
Both use ... | Supervised clustering or classification? | My naive understanding is that classification is performed where you have a specified set of classes and you want to classify a new thing/dataset into one of those specified classes.
Alternatively, cl | Supervised clustering or classification?
My naive understanding is that classification is performed where you have a specified set of classes and you want to classify a new thing/dataset into one of those specified classes.
Alternatively, clustering has nothing to start with and you use all the data (including the new ... | Supervised clustering or classification?
My naive understanding is that classification is performed where you have a specified set of classes and you want to classify a new thing/dataset into one of those specified classes.
Alternatively, cl |
9,309 | Supervised clustering or classification? | I don't think I know more than you do, but the links you posted do suggest answers. I'll take http://www.cs.cornell.edu/~tomf/publications/supervised_kmeans-08.pdf as an example. Basically they state: 1) clustering depends on a distance. 2) successful use of k-means requires a carefully chosen distance. 3) Given traini... | Supervised clustering or classification? | I don't think I know more than you do, but the links you posted do suggest answers. I'll take http://www.cs.cornell.edu/~tomf/publications/supervised_kmeans-08.pdf as an example. Basically they state: | Supervised clustering or classification?
I don't think I know more than you do, but the links you posted do suggest answers. I'll take http://www.cs.cornell.edu/~tomf/publications/supervised_kmeans-08.pdf as an example. Basically they state: 1) clustering depends on a distance. 2) successful use of k-means requires a c... | Supervised clustering or classification?
I don't think I know more than you do, but the links you posted do suggest answers. I'll take http://www.cs.cornell.edu/~tomf/publications/supervised_kmeans-08.pdf as an example. Basically they state: |
9,310 | Supervised clustering or classification? | Some definitions:
Supervised clustering is applied on classified examples with the objective of identifying clusters that have high probability density to a single class.
Unsupervised clustering is a learning framework using a specific object functions, for example a function that minimizes the distances inside a clust... | Supervised clustering or classification? | Some definitions:
Supervised clustering is applied on classified examples with the objective of identifying clusters that have high probability density to a single class.
Unsupervised clustering is a | Supervised clustering or classification?
Some definitions:
Supervised clustering is applied on classified examples with the objective of identifying clusters that have high probability density to a single class.
Unsupervised clustering is a learning framework using a specific object functions, for example a function th... | Supervised clustering or classification?
Some definitions:
Supervised clustering is applied on classified examples with the objective of identifying clusters that have high probability density to a single class.
Unsupervised clustering is a |
9,311 | Supervised clustering or classification? | My interpretation has to do with the number of training samples you have per class.
If you have a lot of training samples per class, then you can reasonably train a classifier and you have a classification use case.
If you only have training samples for a fraction of the classes then a classifier would have poor perfor... | Supervised clustering or classification? | My interpretation has to do with the number of training samples you have per class.
If you have a lot of training samples per class, then you can reasonably train a classifier and you have a classific | Supervised clustering or classification?
My interpretation has to do with the number of training samples you have per class.
If you have a lot of training samples per class, then you can reasonably train a classifier and you have a classification use case.
If you only have training samples for a fraction of the classes... | Supervised clustering or classification?
My interpretation has to do with the number of training samples you have per class.
If you have a lot of training samples per class, then you can reasonably train a classifier and you have a classific |
9,312 | Computation of the marginal likelihood from MCMC samples | The extension by Chib and Jeliazkov (2001) unfortunately gets quickly costly or highly variable, which is a reason why it is not much used outside Gibbs sampling cases.
While there are many ways and approaches to the normalisation constant $\mathfrak{Z}$ estimation problem (as illustrated by the quite diverse talks in ... | Computation of the marginal likelihood from MCMC samples | The extension by Chib and Jeliazkov (2001) unfortunately gets quickly costly or highly variable, which is a reason why it is not much used outside Gibbs sampling cases.
While there are many ways and a | Computation of the marginal likelihood from MCMC samples
The extension by Chib and Jeliazkov (2001) unfortunately gets quickly costly or highly variable, which is a reason why it is not much used outside Gibbs sampling cases.
While there are many ways and approaches to the normalisation constant $\mathfrak{Z}$ estimati... | Computation of the marginal likelihood from MCMC samples
The extension by Chib and Jeliazkov (2001) unfortunately gets quickly costly or highly variable, which is a reason why it is not much used outside Gibbs sampling cases.
While there are many ways and a |
9,313 | Confidence interval of RMSE | I might be able to give an answer to your question under certain conditions.
Let $x_{i}$ be your true value for the $i^{th}$ data point and $\hat{x}_{i}$ the estimated value. If we assume that the differences between the estimated and true values have
mean zero (i.e. the $\hat{x}_{i}$ are distributed around $x_{i}$)
... | Confidence interval of RMSE | I might be able to give an answer to your question under certain conditions.
Let $x_{i}$ be your true value for the $i^{th}$ data point and $\hat{x}_{i}$ the estimated value. If we assume that the dif | Confidence interval of RMSE
I might be able to give an answer to your question under certain conditions.
Let $x_{i}$ be your true value for the $i^{th}$ data point and $\hat{x}_{i}$ the estimated value. If we assume that the differences between the estimated and true values have
mean zero (i.e. the $\hat{x}_{i}$ are d... | Confidence interval of RMSE
I might be able to give an answer to your question under certain conditions.
Let $x_{i}$ be your true value for the $i^{th}$ data point and $\hat{x}_{i}$ the estimated value. If we assume that the dif |
9,314 | Confidence interval of RMSE | The reasoning in the answer by fabee seems correct if applied to the STDE (standard deviation of the error), not the RMSE.
Using similar nomenclature, $i=1,\,\ldots,\,n$ is an index representing each record of data, $x_i$ is the true value and $\hat{x}_i$ is a measurement or prediction.
The error $\epsilon_i$, BIAS, MS... | Confidence interval of RMSE | The reasoning in the answer by fabee seems correct if applied to the STDE (standard deviation of the error), not the RMSE.
Using similar nomenclature, $i=1,\,\ldots,\,n$ is an index representing each | Confidence interval of RMSE
The reasoning in the answer by fabee seems correct if applied to the STDE (standard deviation of the error), not the RMSE.
Using similar nomenclature, $i=1,\,\ldots,\,n$ is an index representing each record of data, $x_i$ is the true value and $\hat{x}_i$ is a measurement or prediction.
The ... | Confidence interval of RMSE
The reasoning in the answer by fabee seems correct if applied to the STDE (standard deviation of the error), not the RMSE.
Using similar nomenclature, $i=1,\,\ldots,\,n$ is an index representing each |
9,315 | Confidence interval of RMSE | Following Faaber 1999, the uncertainty of RMSE is given as
$$\sigma (\hat{RMSE})/RMSE = \sqrt{\frac{1}{2n}}$$
where $n$ is the number of datapoints. | Confidence interval of RMSE | Following Faaber 1999, the uncertainty of RMSE is given as
$$\sigma (\hat{RMSE})/RMSE = \sqrt{\frac{1}{2n}}$$
where $n$ is the number of datapoints. | Confidence interval of RMSE
Following Faaber 1999, the uncertainty of RMSE is given as
$$\sigma (\hat{RMSE})/RMSE = \sqrt{\frac{1}{2n}}$$
where $n$ is the number of datapoints. | Confidence interval of RMSE
Following Faaber 1999, the uncertainty of RMSE is given as
$$\sigma (\hat{RMSE})/RMSE = \sqrt{\frac{1}{2n}}$$
where $n$ is the number of datapoints. |
9,316 | Confidence interval of RMSE | Borrowing code from @Bryan Shalloway's link (https://gist.github.com/brshallo/7eed49c743ac165ced2294a70e73e65e, which is in the comment in the accepted answer), you can calculate this in R with the RMSE value and the degrees of freedom, which @fabee suggests is n (not n-1) in this case.
The R function:
rmse_interval <... | Confidence interval of RMSE | Borrowing code from @Bryan Shalloway's link (https://gist.github.com/brshallo/7eed49c743ac165ced2294a70e73e65e, which is in the comment in the accepted answer), you can calculate this in R with the RM | Confidence interval of RMSE
Borrowing code from @Bryan Shalloway's link (https://gist.github.com/brshallo/7eed49c743ac165ced2294a70e73e65e, which is in the comment in the accepted answer), you can calculate this in R with the RMSE value and the degrees of freedom, which @fabee suggests is n (not n-1) in this case.
The... | Confidence interval of RMSE
Borrowing code from @Bryan Shalloway's link (https://gist.github.com/brshallo/7eed49c743ac165ced2294a70e73e65e, which is in the comment in the accepted answer), you can calculate this in R with the RM |
9,317 | Wald test in regression (OLS and GLMs): t- vs. z-distribution | The output from glm using a Poisson distribution gives a $z$-value because with a Poisson distribution, the mean and variance parameter are the same. In the Poisson model, you only have to estimate a single parameter ($\lambda$). In a glm where you have to estimate both a mean and dispersion parameter, you should see t... | Wald test in regression (OLS and GLMs): t- vs. z-distribution | The output from glm using a Poisson distribution gives a $z$-value because with a Poisson distribution, the mean and variance parameter are the same. In the Poisson model, you only have to estimate a | Wald test in regression (OLS and GLMs): t- vs. z-distribution
The output from glm using a Poisson distribution gives a $z$-value because with a Poisson distribution, the mean and variance parameter are the same. In the Poisson model, you only have to estimate a single parameter ($\lambda$). In a glm where you have to e... | Wald test in regression (OLS and GLMs): t- vs. z-distribution
The output from glm using a Poisson distribution gives a $z$-value because with a Poisson distribution, the mean and variance parameter are the same. In the Poisson model, you only have to estimate a |
9,318 | Wald test in regression (OLS and GLMs): t- vs. z-distribution | In the GLM framework, in general, the W test statistic you mentioned is asymptotically Normal distributed, that's why you see in R the z values.
In addition to that, when dealing with a linear model, i.e a GLM with a Normal distributed response variable, the distribution of test statistic is a Student's t, so in R you ... | Wald test in regression (OLS and GLMs): t- vs. z-distribution | In the GLM framework, in general, the W test statistic you mentioned is asymptotically Normal distributed, that's why you see in R the z values.
In addition to that, when dealing with a linear model, | Wald test in regression (OLS and GLMs): t- vs. z-distribution
In the GLM framework, in general, the W test statistic you mentioned is asymptotically Normal distributed, that's why you see in R the z values.
In addition to that, when dealing with a linear model, i.e a GLM with a Normal distributed response variable, the... | Wald test in regression (OLS and GLMs): t- vs. z-distribution
In the GLM framework, in general, the W test statistic you mentioned is asymptotically Normal distributed, that's why you see in R the z values.
In addition to that, when dealing with a linear model, |
9,319 | What are the impacts of choosing different loss functions in classification to approximate 0-1 loss | Some of my thoughts, may not be correct though.
I understand the reason we have such design (for hinge and logistic loss) is we want the objective function to be convex.
Convexity is surely a nice property, but I think the most important reason is we want the objective function to have non-zero derivatives, so that w... | What are the impacts of choosing different loss functions in classification to approximate 0-1 loss | Some of my thoughts, may not be correct though.
I understand the reason we have such design (for hinge and logistic loss) is we want the objective function to be convex.
Convexity is surely a nice p | What are the impacts of choosing different loss functions in classification to approximate 0-1 loss
Some of my thoughts, may not be correct though.
I understand the reason we have such design (for hinge and logistic loss) is we want the objective function to be convex.
Convexity is surely a nice property, but I think... | What are the impacts of choosing different loss functions in classification to approximate 0-1 loss
Some of my thoughts, may not be correct though.
I understand the reason we have such design (for hinge and logistic loss) is we want the objective function to be convex.
Convexity is surely a nice p |
9,320 | What are the impacts of choosing different loss functions in classification to approximate 0-1 loss | Posting a late reply, since there is a very simple answer which has not been mentioned yet.
what are the prices we need to pay by using different "proxy loss functions", such as hinge loss and logistic loss?
When you replace the non-convex 0-1 loss function by a convex
surrogate (e.g hinge-loss), you are actually no... | What are the impacts of choosing different loss functions in classification to approximate 0-1 loss | Posting a late reply, since there is a very simple answer which has not been mentioned yet.
what are the prices we need to pay by using different "proxy loss functions", such as hinge loss and logis | What are the impacts of choosing different loss functions in classification to approximate 0-1 loss
Posting a late reply, since there is a very simple answer which has not been mentioned yet.
what are the prices we need to pay by using different "proxy loss functions", such as hinge loss and logistic loss?
When you ... | What are the impacts of choosing different loss functions in classification to approximate 0-1 loss
Posting a late reply, since there is a very simple answer which has not been mentioned yet.
what are the prices we need to pay by using different "proxy loss functions", such as hinge loss and logis |
9,321 | What are the impacts of choosing different loss functions in classification to approximate 0-1 loss | Ideally your loss function should reflect actual loss incurred by business. For instance, if you're classifying damaged goods, then the loss of misclassification could be like this:
marking damaged goods that were not: lost profit on potential sale
not marking damaged goods that were damaged: cost of return processing | What are the impacts of choosing different loss functions in classification to approximate 0-1 loss | Ideally your loss function should reflect actual loss incurred by business. For instance, if you're classifying damaged goods, then the loss of misclassification could be like this:
marking damaged g | What are the impacts of choosing different loss functions in classification to approximate 0-1 loss
Ideally your loss function should reflect actual loss incurred by business. For instance, if you're classifying damaged goods, then the loss of misclassification could be like this:
marking damaged goods that were not: ... | What are the impacts of choosing different loss functions in classification to approximate 0-1 loss
Ideally your loss function should reflect actual loss incurred by business. For instance, if you're classifying damaged goods, then the loss of misclassification could be like this:
marking damaged g |
9,322 | How to initialize the elements of the filter matrix? | One typically initializes a network from a random distribution, typically mean zero and some care is taken with regards to choosing its variance. These days with advances in optimization techniques (SGD+Momentum among other methods) and activation nonlinearities (ReLUs and ReLU-like activations allow for better backpro... | How to initialize the elements of the filter matrix? | One typically initializes a network from a random distribution, typically mean zero and some care is taken with regards to choosing its variance. These days with advances in optimization techniques (S | How to initialize the elements of the filter matrix?
One typically initializes a network from a random distribution, typically mean zero and some care is taken with regards to choosing its variance. These days with advances in optimization techniques (SGD+Momentum among other methods) and activation nonlinearities (ReL... | How to initialize the elements of the filter matrix?
One typically initializes a network from a random distribution, typically mean zero and some care is taken with regards to choosing its variance. These days with advances in optimization techniques (S |
9,323 | How to initialize the elements of the filter matrix? | I can't comment because of low reputation and so I am writing this in response to Felipe Almeida's question. After Indie AI's perfect answer, there is nothing much to add. If you want to detect specific shapes (like an X), you can pre define a specific filter, as is the case with edge detection. But this is the beauty ... | How to initialize the elements of the filter matrix? | I can't comment because of low reputation and so I am writing this in response to Felipe Almeida's question. After Indie AI's perfect answer, there is nothing much to add. If you want to detect specif | How to initialize the elements of the filter matrix?
I can't comment because of low reputation and so I am writing this in response to Felipe Almeida's question. After Indie AI's perfect answer, there is nothing much to add. If you want to detect specific shapes (like an X), you can pre define a specific filter, as is ... | How to initialize the elements of the filter matrix?
I can't comment because of low reputation and so I am writing this in response to Felipe Almeida's question. After Indie AI's perfect answer, there is nothing much to add. If you want to detect specif |
9,324 | Python module for change point analysis | You can try out the changefinder library on PyPI. The description says that it's an online Change Detection Library based on the ChangeFinder algorithm
There are also some Python implementations of Michele Basseville's Statistical Change Point Detection techniques available in tutorial format on this Github repo. | Python module for change point analysis | You can try out the changefinder library on PyPI. The description says that it's an online Change Detection Library based on the ChangeFinder algorithm
There are also some Python implementations of Mi | Python module for change point analysis
You can try out the changefinder library on PyPI. The description says that it's an online Change Detection Library based on the ChangeFinder algorithm
There are also some Python implementations of Michele Basseville's Statistical Change Point Detection techniques available in tu... | Python module for change point analysis
You can try out the changefinder library on PyPI. The description says that it's an online Change Detection Library based on the ChangeFinder algorithm
There are also some Python implementations of Mi |
9,325 | Python module for change point analysis | There are still some gaps in the Python library for using advanced statistics packages. Have you tried using the RPy module? When using RPy you can load R modules.
brief tutorial on RPy: http://www.sciprogblog.com/2012/08/using-r-from-within-python.html strucchange | Python module for change point analysis | There are still some gaps in the Python library for using advanced statistics packages. Have you tried using the RPy module? When using RPy you can load R modules.
brief tutorial on RPy: http://www.sc | Python module for change point analysis
There are still some gaps in the Python library for using advanced statistics packages. Have you tried using the RPy module? When using RPy you can load R modules.
brief tutorial on RPy: http://www.sciprogblog.com/2012/08/using-r-from-within-python.html strucchange | Python module for change point analysis
There are still some gaps in the Python library for using advanced statistics packages. Have you tried using the RPy module? When using RPy you can load R modules.
brief tutorial on RPy: http://www.sc |
9,326 | Python module for change point analysis | I just came across a change point detection library in Python named "ruptures" : https://arxiv.org/abs/1801.00826
Maybe this can be of use. | Python module for change point analysis | I just came across a change point detection library in Python named "ruptures" : https://arxiv.org/abs/1801.00826
Maybe this can be of use. | Python module for change point analysis
I just came across a change point detection library in Python named "ruptures" : https://arxiv.org/abs/1801.00826
Maybe this can be of use. | Python module for change point analysis
I just came across a change point detection library in Python named "ruptures" : https://arxiv.org/abs/1801.00826
Maybe this can be of use. |
9,327 | Python module for change point analysis | This implementation of the Python package rpy2 worked for me:
import numpy as np
from rpy2.robjects.packages import importr
import rpy2.robjects as robjects
r = robjects.r #allows access to r object with r.
bcp = importr('bcp') #import bayesian change point package in python
values = bcp.bcp( r.c( r.rnorm(50) , r.r... | Python module for change point analysis | This implementation of the Python package rpy2 worked for me:
import numpy as np
from rpy2.robjects.packages import importr
import rpy2.robjects as robjects
r = robjects.r #allows access to r object | Python module for change point analysis
This implementation of the Python package rpy2 worked for me:
import numpy as np
from rpy2.robjects.packages import importr
import rpy2.robjects as robjects
r = robjects.r #allows access to r object with r.
bcp = importr('bcp') #import bayesian change point package in python
... | Python module for change point analysis
This implementation of the Python package rpy2 worked for me:
import numpy as np
from rpy2.robjects.packages import importr
import rpy2.robjects as robjects
r = robjects.r #allows access to r object |
9,328 | Python module for change point analysis | Depending on your requirement for online/offline change point detection, python has the below packages:
1) The ruptures package, a Python library for performing offline change point detection.
2) Calling the R changepoint package into Python using the rpy2 package, an R-to-Python interface.
3) The changefinder pack... | Python module for change point analysis | Depending on your requirement for online/offline change point detection, python has the below packages:
1) The ruptures package, a Python library for performing offline change point detection.
2) Ca | Python module for change point analysis
Depending on your requirement for online/offline change point detection, python has the below packages:
1) The ruptures package, a Python library for performing offline change point detection.
2) Calling the R changepoint package into Python using the rpy2 package, an R-to-Pyth... | Python module for change point analysis
Depending on your requirement for online/offline change point detection, python has the below packages:
1) The ruptures package, a Python library for performing offline change point detection.
2) Ca |
9,329 | Python module for change point analysis | Have you tried ChangeFinder library, you can install it on linux by:
pip install changefinder
also Bayesian_changepoint_detection GitHub code can be found here: GitHub Code | Python module for change point analysis | Have you tried ChangeFinder library, you can install it on linux by:
pip install changefinder
also Bayesian_changepoint_detection GitHub code can be found here: GitHub Code | Python module for change point analysis
Have you tried ChangeFinder library, you can install it on linux by:
pip install changefinder
also Bayesian_changepoint_detection GitHub code can be found here: GitHub Code | Python module for change point analysis
Have you tried ChangeFinder library, you can install it on linux by:
pip install changefinder
also Bayesian_changepoint_detection GitHub code can be found here: GitHub Code |
9,330 | Kolmogorov–Smirnov test vs. t-test | As an example of why you'd want to use the two sample Kolmogorov-Smirnov test:
Imagine that the population means were similar but the variances were very different. The Kolmogorov-Smirnov test could pick this difference up but the t-test cannot.
Or imagine that the distributions have similar means and sd's but the male... | Kolmogorov–Smirnov test vs. t-test | As an example of why you'd want to use the two sample Kolmogorov-Smirnov test:
Imagine that the population means were similar but the variances were very different. The Kolmogorov-Smirnov test could p | Kolmogorov–Smirnov test vs. t-test
As an example of why you'd want to use the two sample Kolmogorov-Smirnov test:
Imagine that the population means were similar but the variances were very different. The Kolmogorov-Smirnov test could pick this difference up but the t-test cannot.
Or imagine that the distributions have ... | Kolmogorov–Smirnov test vs. t-test
As an example of why you'd want to use the two sample Kolmogorov-Smirnov test:
Imagine that the population means were similar but the variances were very different. The Kolmogorov-Smirnov test could p |
9,331 | Distant supervision: supervised, semi-supervised, or both? | A Distant supervision algorithm usually has the following steps:
1] It may have some labeled training data
2] It "has" access to a pool of unlabeled data
3] It has an operator that allows it to sample from this unlabeled data and label them and this operator is expected to be noisy in its labels
4] The algorithm th... | Distant supervision: supervised, semi-supervised, or both? | A Distant supervision algorithm usually has the following steps:
1] It may have some labeled training data
2] It "has" access to a pool of unlabeled data
3] It has an operator that allows it to sam | Distant supervision: supervised, semi-supervised, or both?
A Distant supervision algorithm usually has the following steps:
1] It may have some labeled training data
2] It "has" access to a pool of unlabeled data
3] It has an operator that allows it to sample from this unlabeled data and label them and this operator... | Distant supervision: supervised, semi-supervised, or both?
A Distant supervision algorithm usually has the following steps:
1] It may have some labeled training data
2] It "has" access to a pool of unlabeled data
3] It has an operator that allows it to sam |
9,332 | Relu vs Sigmoid vs Softmax as hidden layer neurons | In addition to @Bhagyesh_Vikani:
Relu behaves close to a linear unit
Relu is like a switch for linearity. If you don't need it, you "switch" it off. If you need it, you "switch" it on. Thus, we get the linearity benefits but reserve ourself an option of not using it altogther.
The derivative is 1 when it's active. The... | Relu vs Sigmoid vs Softmax as hidden layer neurons | In addition to @Bhagyesh_Vikani:
Relu behaves close to a linear unit
Relu is like a switch for linearity. If you don't need it, you "switch" it off. If you need it, you "switch" it on. Thus, we get t | Relu vs Sigmoid vs Softmax as hidden layer neurons
In addition to @Bhagyesh_Vikani:
Relu behaves close to a linear unit
Relu is like a switch for linearity. If you don't need it, you "switch" it off. If you need it, you "switch" it on. Thus, we get the linearity benefits but reserve ourself an option of not using it a... | Relu vs Sigmoid vs Softmax as hidden layer neurons
In addition to @Bhagyesh_Vikani:
Relu behaves close to a linear unit
Relu is like a switch for linearity. If you don't need it, you "switch" it off. If you need it, you "switch" it on. Thus, we get t |
9,333 | Relu vs Sigmoid vs Softmax as hidden layer neurons | Relu have its own pros and cons:
Pros:
1. Does not saturate (in +ve region)
2. Computationally, it is very efficient
3. Generally models with relu neurons converge much faster than neurons with other activation functions, as described here
Cons:
1. One issue with dealing with them is where they die, i.e. dead ... | Relu vs Sigmoid vs Softmax as hidden layer neurons | Relu have its own pros and cons:
Pros:
1. Does not saturate (in +ve region)
2. Computationally, it is very efficient
3. Generally models with relu neurons converge much faster than neurons with | Relu vs Sigmoid vs Softmax as hidden layer neurons
Relu have its own pros and cons:
Pros:
1. Does not saturate (in +ve region)
2. Computationally, it is very efficient
3. Generally models with relu neurons converge much faster than neurons with other activation functions, as described here
Cons:
1. One issue w... | Relu vs Sigmoid vs Softmax as hidden layer neurons
Relu have its own pros and cons:
Pros:
1. Does not saturate (in +ve region)
2. Computationally, it is very efficient
3. Generally models with relu neurons converge much faster than neurons with |
9,334 | Relu vs Sigmoid vs Softmax as hidden layer neurons | http://cs231n.github.io/neural-networks-1/
Sigmoids
Sigmoids saturate and kill gradients.
Sigmoid outputs are not zero-centered.
tanh
Like the sigmoid neuron, its activations saturate, but unlike the sigmoid neuron its output is zero-centered. Therefore, in practice the tanh non-linearity is always preferred to the s... | Relu vs Sigmoid vs Softmax as hidden layer neurons | http://cs231n.github.io/neural-networks-1/
Sigmoids
Sigmoids saturate and kill gradients.
Sigmoid outputs are not zero-centered.
tanh
Like the sigmoid neuron, its activations saturate, but unlike th | Relu vs Sigmoid vs Softmax as hidden layer neurons
http://cs231n.github.io/neural-networks-1/
Sigmoids
Sigmoids saturate and kill gradients.
Sigmoid outputs are not zero-centered.
tanh
Like the sigmoid neuron, its activations saturate, but unlike the sigmoid neuron its output is zero-centered. Therefore, in practice ... | Relu vs Sigmoid vs Softmax as hidden layer neurons
http://cs231n.github.io/neural-networks-1/
Sigmoids
Sigmoids saturate and kill gradients.
Sigmoid outputs are not zero-centered.
tanh
Like the sigmoid neuron, its activations saturate, but unlike th |
9,335 | How well does R scale to text classification tasks? [closed] | As requested in a comment, here are some pointers for processing steps. A number of tools may be found at the CRAN Task View for Natural Language Processing. You may also want to look at this paper on the tm (text mining) package for R.
Prior to processing, consider normalization of the word tokens. openNLP (for wh... | How well does R scale to text classification tasks? [closed] | As requested in a comment, here are some pointers for processing steps. A number of tools may be found at the CRAN Task View for Natural Language Processing. You may also want to look at this paper | How well does R scale to text classification tasks? [closed]
As requested in a comment, here are some pointers for processing steps. A number of tools may be found at the CRAN Task View for Natural Language Processing. You may also want to look at this paper on the tm (text mining) package for R.
Prior to processing... | How well does R scale to text classification tasks? [closed]
As requested in a comment, here are some pointers for processing steps. A number of tools may be found at the CRAN Task View for Natural Language Processing. You may also want to look at this paper |
9,336 | How well does R scale to text classification tasks? [closed] | First, welcome! Text processing is lots of fun, and doing it in R is getting easier all the time.
The short answer: yes - the tools in R are now quite good for dealing with this kind of data. In fact, there's nothing special about R, C++, Groovy, Scala, or any other language when it comes to data storage in RAM: ever... | How well does R scale to text classification tasks? [closed] | First, welcome! Text processing is lots of fun, and doing it in R is getting easier all the time.
The short answer: yes - the tools in R are now quite good for dealing with this kind of data. In fac | How well does R scale to text classification tasks? [closed]
First, welcome! Text processing is lots of fun, and doing it in R is getting easier all the time.
The short answer: yes - the tools in R are now quite good for dealing with this kind of data. In fact, there's nothing special about R, C++, Groovy, Scala, or ... | How well does R scale to text classification tasks? [closed]
First, welcome! Text processing is lots of fun, and doing it in R is getting easier all the time.
The short answer: yes - the tools in R are now quite good for dealing with this kind of data. In fac |
9,337 | How well does R scale to text classification tasks? [closed] | I agree with crayola that the number of rows is crucial here. For RF you will need at least 3x more RAM than your dataset weights and probably a lot of time (such number of attributes usually requires a lot of trees in the forest -- and note that there is no parallel implementation of RF in R).
About SVM, I doubt it is... | How well does R scale to text classification tasks? [closed] | I agree with crayola that the number of rows is crucial here. For RF you will need at least 3x more RAM than your dataset weights and probably a lot of time (such number of attributes usually requires | How well does R scale to text classification tasks? [closed]
I agree with crayola that the number of rows is crucial here. For RF you will need at least 3x more RAM than your dataset weights and probably a lot of time (such number of attributes usually requires a lot of trees in the forest -- and note that there is no ... | How well does R scale to text classification tasks? [closed]
I agree with crayola that the number of rows is crucial here. For RF you will need at least 3x more RAM than your dataset weights and probably a lot of time (such number of attributes usually requires |
9,338 | How to interpret these acf and pacf plots | looking at plots in order to try to pigeonhole the data into a guessed arima model works well when 1: There are no outliers/pulses/level shifts, local time trends and no seasonal deterministic pulses in the data AND 2) when the arima model has constant parameters over time AND 3) when the error variance from the arima... | How to interpret these acf and pacf plots | looking at plots in order to try to pigeonhole the data into a guessed arima model works well when 1: There are no outliers/pulses/level shifts, local time trends and no seasonal deterministic pulses | How to interpret these acf and pacf plots
looking at plots in order to try to pigeonhole the data into a guessed arima model works well when 1: There are no outliers/pulses/level shifts, local time trends and no seasonal deterministic pulses in the data AND 2) when the arima model has constant parameters over time AND ... | How to interpret these acf and pacf plots
looking at plots in order to try to pigeonhole the data into a guessed arima model works well when 1: There are no outliers/pulses/level shifts, local time trends and no seasonal deterministic pulses |
9,339 | How to interpret these acf and pacf plots | Interpretation of the ACF and PACF
The slow decay of the autocorrelation function suggests the data follow a long-memory process. The duration of shocks is relatively persistent and influence the data several observations ahead. This is probably reflected by a
smooth trending pattern in the data.
The ACF and PACF of o... | How to interpret these acf and pacf plots | Interpretation of the ACF and PACF
The slow decay of the autocorrelation function suggests the data follow a long-memory process. The duration of shocks is relatively persistent and influence the data | How to interpret these acf and pacf plots
Interpretation of the ACF and PACF
The slow decay of the autocorrelation function suggests the data follow a long-memory process. The duration of shocks is relatively persistent and influence the data several observations ahead. This is probably reflected by a
smooth trending ... | How to interpret these acf and pacf plots
Interpretation of the ACF and PACF
The slow decay of the autocorrelation function suggests the data follow a long-memory process. The duration of shocks is relatively persistent and influence the data |
9,340 | Choosing a bandwidth for kernel density estimators | For a univariate KDE, you are better off using something other than Silverman's rule which is based on a normal approximation. One excellent approach is the Sheather-Jones method, easily implemented in R; for example,
plot(density(precip, bw="SJ"))
The situation for multivariate KDE is not so well studied, and the too... | Choosing a bandwidth for kernel density estimators | For a univariate KDE, you are better off using something other than Silverman's rule which is based on a normal approximation. One excellent approach is the Sheather-Jones method, easily implemented i | Choosing a bandwidth for kernel density estimators
For a univariate KDE, you are better off using something other than Silverman's rule which is based on a normal approximation. One excellent approach is the Sheather-Jones method, easily implemented in R; for example,
plot(density(precip, bw="SJ"))
The situation for m... | Choosing a bandwidth for kernel density estimators
For a univariate KDE, you are better off using something other than Silverman's rule which is based on a normal approximation. One excellent approach is the Sheather-Jones method, easily implemented i |
9,341 | Choosing a bandwidth for kernel density estimators | For univariate kernel density estimation, the bandwidth can be estimated by Normal reference rule or Cross Validation method or plug-in approach.
For multivariate kernel density estimation, a Bayesian bandwidth selection method may be utilized, see Zhang, X., M.L. King and R.J. Hyndman (2006), A Bayesian approach to b... | Choosing a bandwidth for kernel density estimators | For univariate kernel density estimation, the bandwidth can be estimated by Normal reference rule or Cross Validation method or plug-in approach.
For multivariate kernel density estimation, a Bayesia | Choosing a bandwidth for kernel density estimators
For univariate kernel density estimation, the bandwidth can be estimated by Normal reference rule or Cross Validation method or plug-in approach.
For multivariate kernel density estimation, a Bayesian bandwidth selection method may be utilized, see Zhang, X., M.L. Kin... | Choosing a bandwidth for kernel density estimators
For univariate kernel density estimation, the bandwidth can be estimated by Normal reference rule or Cross Validation method or plug-in approach.
For multivariate kernel density estimation, a Bayesia |
9,342 | Clustering a binary matrix | Latent class analysis is one possible approach.
Take the following probability distribution where A, B, and C can take on values of 1 or 0.
$P(A_i, B_j, C_k)$
If these were independent of each other, then we would expect to see:
$P(A_i, B_j, C_k)=P(A_i)P(B_j)P(C_k)$
Once this possiblity is eliminated, we might hypoth... | Clustering a binary matrix | Latent class analysis is one possible approach.
Take the following probability distribution where A, B, and C can take on values of 1 or 0.
$P(A_i, B_j, C_k)$
If these were independent of each other, | Clustering a binary matrix
Latent class analysis is one possible approach.
Take the following probability distribution where A, B, and C can take on values of 1 or 0.
$P(A_i, B_j, C_k)$
If these were independent of each other, then we would expect to see:
$P(A_i, B_j, C_k)=P(A_i)P(B_j)P(C_k)$
Once this possiblity is ... | Clustering a binary matrix
Latent class analysis is one possible approach.
Take the following probability distribution where A, B, and C can take on values of 1 or 0.
$P(A_i, B_j, C_k)$
If these were independent of each other, |
9,343 | Clustering a binary matrix | Actually, frequent itemset mining may be a better choice than clustering on such data.
The usual vector-oriented set of algorithms does not make a lot of sense. K-means for example will produce means that are no longer binary. | Clustering a binary matrix | Actually, frequent itemset mining may be a better choice than clustering on such data.
The usual vector-oriented set of algorithms does not make a lot of sense. K-means for example will produce means | Clustering a binary matrix
Actually, frequent itemset mining may be a better choice than clustering on such data.
The usual vector-oriented set of algorithms does not make a lot of sense. K-means for example will produce means that are no longer binary. | Clustering a binary matrix
Actually, frequent itemset mining may be a better choice than clustering on such data.
The usual vector-oriented set of algorithms does not make a lot of sense. K-means for example will produce means |
9,344 | Is PCA unstable under multicollinearity? | The answer might be given in even simpler terms: the multiple regression has one step more than the pca if seen in terms of linear algebra, and from the second step the instability comes into existence:
The first step of pca and mult. regression can be seen as factoring of the correlation-matrix $R$ into two chol... | Is PCA unstable under multicollinearity? | The answer might be given in even simpler terms: the multiple regression has one step more than the pca if seen in terms of linear algebra, and from the second step the instability comes into existenc | Is PCA unstable under multicollinearity?
The answer might be given in even simpler terms: the multiple regression has one step more than the pca if seen in terms of linear algebra, and from the second step the instability comes into existence:
The first step of pca and mult. regression can be seen as factoring of... | Is PCA unstable under multicollinearity?
The answer might be given in even simpler terms: the multiple regression has one step more than the pca if seen in terms of linear algebra, and from the second step the instability comes into existenc |
9,345 | Is PCA unstable under multicollinearity? | PCA is often a means to an ends; leading up to either inputs to a multiple regression or for use in a cluster analysis. I think in your case, you are talking about using the results of a PCA to perform a regression.
In that case, your objective of performing a PCA is to get rid of mulitcollinearity and get orthogonal i... | Is PCA unstable under multicollinearity? | PCA is often a means to an ends; leading up to either inputs to a multiple regression or for use in a cluster analysis. I think in your case, you are talking about using the results of a PCA to perfor | Is PCA unstable under multicollinearity?
PCA is often a means to an ends; leading up to either inputs to a multiple regression or for use in a cluster analysis. I think in your case, you are talking about using the results of a PCA to perform a regression.
In that case, your objective of performing a PCA is to get rid ... | Is PCA unstable under multicollinearity?
PCA is often a means to an ends; leading up to either inputs to a multiple regression or for use in a cluster analysis. I think in your case, you are talking about using the results of a PCA to perfor |
9,346 | Variance estimates in k-fold cross-validation | Very interesting question, I'll have to read the papers you give... But maybe this will start us in direction of an answer:
I usually tackle this problem in a very pragmatic way: I iterate the k-fold cross validation with new random splits and calculate performance just as usual for each iteration. The overall test sam... | Variance estimates in k-fold cross-validation | Very interesting question, I'll have to read the papers you give... But maybe this will start us in direction of an answer:
I usually tackle this problem in a very pragmatic way: I iterate the k-fold | Variance estimates in k-fold cross-validation
Very interesting question, I'll have to read the papers you give... But maybe this will start us in direction of an answer:
I usually tackle this problem in a very pragmatic way: I iterate the k-fold cross validation with new random splits and calculate performance just as ... | Variance estimates in k-fold cross-validation
Very interesting question, I'll have to read the papers you give... But maybe this will start us in direction of an answer:
I usually tackle this problem in a very pragmatic way: I iterate the k-fold |
9,347 | Variance estimates in k-fold cross-validation | Remember CV is an estimate only and can never represent the 'real' generalisation error.
Depending on your sample size (which will impact your number of folds or fold size) you can be severely limited in your ability to calculate any parameter estimates of the distribution of the generalisation error.
In my opinion (an... | Variance estimates in k-fold cross-validation | Remember CV is an estimate only and can never represent the 'real' generalisation error.
Depending on your sample size (which will impact your number of folds or fold size) you can be severely limited | Variance estimates in k-fold cross-validation
Remember CV is an estimate only and can never represent the 'real' generalisation error.
Depending on your sample size (which will impact your number of folds or fold size) you can be severely limited in your ability to calculate any parameter estimates of the distribution ... | Variance estimates in k-fold cross-validation
Remember CV is an estimate only and can never represent the 'real' generalisation error.
Depending on your sample size (which will impact your number of folds or fold size) you can be severely limited |
9,348 | Has the journal Science endorsed the Garden of Forking Pathes Analyses? | There is a blog posting by the authors that describes this at a high level.
To quote from early in that posting:
In order to reduce the number of variables and simplify our task, we
first select some promising looking variables, for example, those that
have a positive correlation with the response variable (systol... | Has the journal Science endorsed the Garden of Forking Pathes Analyses? | There is a blog posting by the authors that describes this at a high level.
To quote from early in that posting:
In order to reduce the number of variables and simplify our task, we
first select so | Has the journal Science endorsed the Garden of Forking Pathes Analyses?
There is a blog posting by the authors that describes this at a high level.
To quote from early in that posting:
In order to reduce the number of variables and simplify our task, we
first select some promising looking variables, for example, tho... | Has the journal Science endorsed the Garden of Forking Pathes Analyses?
There is a blog posting by the authors that describes this at a high level.
To quote from early in that posting:
In order to reduce the number of variables and simplify our task, we
first select so |
9,349 | Has the journal Science endorsed the Garden of Forking Pathes Analyses? | The claim that adding noise helps prevent overfitting really does hold water here, since what they are really doing is limiting how the holdout is reused. Their method actually does two things: it limits the number of questions that can be asked of the holdout, and how much of each of the answers reveals about the hold... | Has the journal Science endorsed the Garden of Forking Pathes Analyses? | The claim that adding noise helps prevent overfitting really does hold water here, since what they are really doing is limiting how the holdout is reused. Their method actually does two things: it lim | Has the journal Science endorsed the Garden of Forking Pathes Analyses?
The claim that adding noise helps prevent overfitting really does hold water here, since what they are really doing is limiting how the holdout is reused. Their method actually does two things: it limits the number of questions that can be asked of... | Has the journal Science endorsed the Garden of Forking Pathes Analyses?
The claim that adding noise helps prevent overfitting really does hold water here, since what they are really doing is limiting how the holdout is reused. Their method actually does two things: it lim |
9,350 | Has the journal Science endorsed the Garden of Forking Pathes Analyses? | I'm sure I'm over-simplifying this differential privacy technique here, but the idea makes sense in a high level.
When you get an algorithm to spit out good result (wow, the accuracy on my test set has really improved), you don't want to jump to conclusion right away. You want to accept it only when the improvement is... | Has the journal Science endorsed the Garden of Forking Pathes Analyses? | I'm sure I'm over-simplifying this differential privacy technique here, but the idea makes sense in a high level.
When you get an algorithm to spit out good result (wow, the accuracy on my test set ha | Has the journal Science endorsed the Garden of Forking Pathes Analyses?
I'm sure I'm over-simplifying this differential privacy technique here, but the idea makes sense in a high level.
When you get an algorithm to spit out good result (wow, the accuracy on my test set has really improved), you don't want to jump to co... | Has the journal Science endorsed the Garden of Forking Pathes Analyses?
I'm sure I'm over-simplifying this differential privacy technique here, but the idea makes sense in a high level.
When you get an algorithm to spit out good result (wow, the accuracy on my test set ha |
9,351 | Has the journal Science endorsed the Garden of Forking Pathes Analyses? | I object to your second sentence. The idea that one's complete plan of data analysis should be determined in advance is unjustified, even in a setting where you are trying to confirm a preexisting scientific hypothesis. On the contrary, any decent data analysis will require some attention to the actual data that has be... | Has the journal Science endorsed the Garden of Forking Pathes Analyses? | I object to your second sentence. The idea that one's complete plan of data analysis should be determined in advance is unjustified, even in a setting where you are trying to confirm a preexisting sci | Has the journal Science endorsed the Garden of Forking Pathes Analyses?
I object to your second sentence. The idea that one's complete plan of data analysis should be determined in advance is unjustified, even in a setting where you are trying to confirm a preexisting scientific hypothesis. On the contrary, any decent ... | Has the journal Science endorsed the Garden of Forking Pathes Analyses?
I object to your second sentence. The idea that one's complete plan of data analysis should be determined in advance is unjustified, even in a setting where you are trying to confirm a preexisting sci |
9,352 | Did Deborah Mayo refute Birnbaum's proof of the likelihood principle? | In a nutshell, Birnbaum's argument is that two widely accepted principles logically imply that the likelihood principle must hold. The counter-argument of Mayo is that the proof is wrong because Birnbaum misuses one of the principles.
Below I simplify the arguments to the extent that they are not very rigorous. My purp... | Did Deborah Mayo refute Birnbaum's proof of the likelihood principle? | In a nutshell, Birnbaum's argument is that two widely accepted principles logically imply that the likelihood principle must hold. The counter-argument of Mayo is that the proof is wrong because Birnb | Did Deborah Mayo refute Birnbaum's proof of the likelihood principle?
In a nutshell, Birnbaum's argument is that two widely accepted principles logically imply that the likelihood principle must hold. The counter-argument of Mayo is that the proof is wrong because Birnbaum misuses one of the principles.
Below I simplif... | Did Deborah Mayo refute Birnbaum's proof of the likelihood principle?
In a nutshell, Birnbaum's argument is that two widely accepted principles logically imply that the likelihood principle must hold. The counter-argument of Mayo is that the proof is wrong because Birnb |
9,353 | Did Deborah Mayo refute Birnbaum's proof of the likelihood principle? | While it is interesting to determine the validity of Birnbaum’s (1962) proof that the sufficiency principle (SP) and one of the versions of the conditionality principle (CP) together imply the likelihood principle (LP), I believe there is a deeper problem with the theorem. Specifically, the CP cannot be justified from ... | Did Deborah Mayo refute Birnbaum's proof of the likelihood principle? | While it is interesting to determine the validity of Birnbaum’s (1962) proof that the sufficiency principle (SP) and one of the versions of the conditionality principle (CP) together imply the likelih | Did Deborah Mayo refute Birnbaum's proof of the likelihood principle?
While it is interesting to determine the validity of Birnbaum’s (1962) proof that the sufficiency principle (SP) and one of the versions of the conditionality principle (CP) together imply the likelihood principle (LP), I believe there is a deeper pr... | Did Deborah Mayo refute Birnbaum's proof of the likelihood principle?
While it is interesting to determine the validity of Birnbaum’s (1962) proof that the sufficiency principle (SP) and one of the versions of the conditionality principle (CP) together imply the likelih |
9,354 | Consequences of the Gaussian correlation inequality for computing joint confidence intervals | I think the question is more of relevance. In some sense, you are looking at multiple hypothesis testing and comparing to running multiple hypothesis tests.
Yes, indeed there is a lower bound which is the product of the p-values of the tests assuming independence. This is the basis for adjustments to p-values in Mult... | Consequences of the Gaussian correlation inequality for computing joint confidence intervals | I think the question is more of relevance. In some sense, you are looking at multiple hypothesis testing and comparing to running multiple hypothesis tests.
Yes, indeed there is a lower bound which | Consequences of the Gaussian correlation inequality for computing joint confidence intervals
I think the question is more of relevance. In some sense, you are looking at multiple hypothesis testing and comparing to running multiple hypothesis tests.
Yes, indeed there is a lower bound which is the product of the p-val... | Consequences of the Gaussian correlation inequality for computing joint confidence intervals
I think the question is more of relevance. In some sense, you are looking at multiple hypothesis testing and comparing to running multiple hypothesis tests.
Yes, indeed there is a lower bound which |
9,355 | How does a Relevance Vector Machine (RVM) work? | $\newcommand{\wv}{\mathbf{w}}
\newcommand{\alv}{\boldsymbol{\alpha}}
\newcommand{\thv}{\boldsymbol{\theta}}
\newcommand{\muv}{\boldsymbol{\mu}}
\newcommand{\ev}{\mathbf{e}}
\newcommand{\fv}{\mathbf{f}}
\newcommand{\Xv}{\mathbf{X}}
\newcommand{\xv}{\mathbf{x}}
\newcommand{\yv}{\mathbf{y}}
\newcommand{\vv}{\mathbf{v}}
$
... | How does a Relevance Vector Machine (RVM) work? | $\newcommand{\wv}{\mathbf{w}}
\newcommand{\alv}{\boldsymbol{\alpha}}
\newcommand{\thv}{\boldsymbol{\theta}}
\newcommand{\muv}{\boldsymbol{\mu}}
\newcommand{\ev}{\mathbf{e}}
\newcommand{\fv}{\mathbf{f} | How does a Relevance Vector Machine (RVM) work?
$\newcommand{\wv}{\mathbf{w}}
\newcommand{\alv}{\boldsymbol{\alpha}}
\newcommand{\thv}{\boldsymbol{\theta}}
\newcommand{\muv}{\boldsymbol{\mu}}
\newcommand{\ev}{\mathbf{e}}
\newcommand{\fv}{\mathbf{f}}
\newcommand{\Xv}{\mathbf{X}}
\newcommand{\xv}{\mathbf{x}}
\newcommand{... | How does a Relevance Vector Machine (RVM) work?
$\newcommand{\wv}{\mathbf{w}}
\newcommand{\alv}{\boldsymbol{\alpha}}
\newcommand{\thv}{\boldsymbol{\theta}}
\newcommand{\muv}{\boldsymbol{\mu}}
\newcommand{\ev}{\mathbf{e}}
\newcommand{\fv}{\mathbf{f} |
9,356 | SVD of correlated matrix should be additive but doesn't appear to be | Note that 'bicluster' in this article refers to a subset of a matrix, "a subset of rows which exhibit similar behavior across a subset of columns, or vice versa." Identification of biclusters is commonly done in data mining algorithms. The authors are prosing a new 'correlated bicluster model' that is different from pr... | SVD of correlated matrix should be additive but doesn't appear to be | Note that 'bicluster' in this article refers to a subset of a matrix, "a subset of rows which exhibit similar behavior across a subset of columns, or vice versa." Identification of biclusters is commo | SVD of correlated matrix should be additive but doesn't appear to be
Note that 'bicluster' in this article refers to a subset of a matrix, "a subset of rows which exhibit similar behavior across a subset of columns, or vice versa." Identification of biclusters is commonly done in data mining algorithms. The authors are... | SVD of correlated matrix should be additive but doesn't appear to be
Note that 'bicluster' in this article refers to a subset of a matrix, "a subset of rows which exhibit similar behavior across a subset of columns, or vice versa." Identification of biclusters is commo |
9,357 | When is binomial distribution function above/below its limiting Poisson distribution function? | With regard to the following:
the mean of a Binomial dist is $np$
the variance is $np(1-p)$
the mean of a Poisson dist is $\lambda$, which we can imagine as $n\times p$
the variance of a Poisson is the same as the mean
Now, if a Poisson is the limit to a Binomial with parameters $n$ and $p$, such that $n$ increases t... | When is binomial distribution function above/below its limiting Poisson distribution function? | With regard to the following:
the mean of a Binomial dist is $np$
the variance is $np(1-p)$
the mean of a Poisson dist is $\lambda$, which we can imagine as $n\times p$
the variance of a Poisson is t | When is binomial distribution function above/below its limiting Poisson distribution function?
With regard to the following:
the mean of a Binomial dist is $np$
the variance is $np(1-p)$
the mean of a Poisson dist is $\lambda$, which we can imagine as $n\times p$
the variance of a Poisson is the same as the mean
Now,... | When is binomial distribution function above/below its limiting Poisson distribution function?
With regard to the following:
the mean of a Binomial dist is $np$
the variance is $np(1-p)$
the mean of a Poisson dist is $\lambda$, which we can imagine as $n\times p$
the variance of a Poisson is t |
9,358 | What are disadvantages of using the lasso for variable selection for regression? | There is NO reason to do stepwise selection. It's just wrong.
LASSO/LAR are the best automatic methods. But they are automatic methods. They let the analyst not think.
In many analyses, some variables should be in the model REGARDLESS of any measure of significance. Sometimes they are necessary control variables. ... | What are disadvantages of using the lasso for variable selection for regression? | There is NO reason to do stepwise selection. It's just wrong.
LASSO/LAR are the best automatic methods. But they are automatic methods. They let the analyst not think.
In many analyses, some variab | What are disadvantages of using the lasso for variable selection for regression?
There is NO reason to do stepwise selection. It's just wrong.
LASSO/LAR are the best automatic methods. But they are automatic methods. They let the analyst not think.
In many analyses, some variables should be in the model REGARDLESS o... | What are disadvantages of using the lasso for variable selection for regression?
There is NO reason to do stepwise selection. It's just wrong.
LASSO/LAR are the best automatic methods. But they are automatic methods. They let the analyst not think.
In many analyses, some variab |
9,359 | What are disadvantages of using the lasso for variable selection for regression? | If you only care about prediction error and don't care about interpretability, casual-inference, model-simplicity, coefficients' tests, etc, why do you still want to use linear regression model?
You can use something like boosting on decision trees or support vector regression and get better prediction quality and sti... | What are disadvantages of using the lasso for variable selection for regression? | If you only care about prediction error and don't care about interpretability, casual-inference, model-simplicity, coefficients' tests, etc, why do you still want to use linear regression model?
You | What are disadvantages of using the lasso for variable selection for regression?
If you only care about prediction error and don't care about interpretability, casual-inference, model-simplicity, coefficients' tests, etc, why do you still want to use linear regression model?
You can use something like boosting on deci... | What are disadvantages of using the lasso for variable selection for regression?
If you only care about prediction error and don't care about interpretability, casual-inference, model-simplicity, coefficients' tests, etc, why do you still want to use linear regression model?
You |
9,360 | What are disadvantages of using the lasso for variable selection for regression? | LASSO encourages shrinking of coefficients to 0, i.e. dropping those variates from your model. On contrast, other regularization techniques like a ridge tend to keep all variates.
So I'd recommend to think about whether this dropping makes sense for your data. E.g. consider setting up a clinical diagnostic test either... | What are disadvantages of using the lasso for variable selection for regression? | LASSO encourages shrinking of coefficients to 0, i.e. dropping those variates from your model. On contrast, other regularization techniques like a ridge tend to keep all variates.
So I'd recommend to | What are disadvantages of using the lasso for variable selection for regression?
LASSO encourages shrinking of coefficients to 0, i.e. dropping those variates from your model. On contrast, other regularization techniques like a ridge tend to keep all variates.
So I'd recommend to think about whether this dropping make... | What are disadvantages of using the lasso for variable selection for regression?
LASSO encourages shrinking of coefficients to 0, i.e. dropping those variates from your model. On contrast, other regularization techniques like a ridge tend to keep all variates.
So I'd recommend to |
9,361 | What are disadvantages of using the lasso for variable selection for regression? | This is already quite an old question but I feel that in the meantime most of the answers here are quite outdated (and the one that's checked as the correct answer is plain wrong imho).
First, in terms of getting good prediction performance it is not universally true that LASSO is always better than stepwise.
The pap... | What are disadvantages of using the lasso for variable selection for regression? | This is already quite an old question but I feel that in the meantime most of the answers here are quite outdated (and the one that's checked as the correct answer is plain wrong imho).
First, in ter | What are disadvantages of using the lasso for variable selection for regression?
This is already quite an old question but I feel that in the meantime most of the answers here are quite outdated (and the one that's checked as the correct answer is plain wrong imho).
First, in terms of getting good prediction performan... | What are disadvantages of using the lasso for variable selection for regression?
This is already quite an old question but I feel that in the meantime most of the answers here are quite outdated (and the one that's checked as the correct answer is plain wrong imho).
First, in ter |
9,362 | What are disadvantages of using the lasso for variable selection for regression? | If two predictors are highly correlated LASSO can end up dropping one rather arbitrarily. That's not very good when you're wanting to make predictions for a population where those two predictors aren't highly correlated, & perhaps a reason for preferring ridge regression in those circumstances.
You might also think sta... | What are disadvantages of using the lasso for variable selection for regression? | If two predictors are highly correlated LASSO can end up dropping one rather arbitrarily. That's not very good when you're wanting to make predictions for a population where those two predictors aren' | What are disadvantages of using the lasso for variable selection for regression?
If two predictors are highly correlated LASSO can end up dropping one rather arbitrarily. That's not very good when you're wanting to make predictions for a population where those two predictors aren't highly correlated, & perhaps a reason... | What are disadvantages of using the lasso for variable selection for regression?
If two predictors are highly correlated LASSO can end up dropping one rather arbitrarily. That's not very good when you're wanting to make predictions for a population where those two predictors aren' |
9,363 | What are disadvantages of using the lasso for variable selection for regression? | Lasso is only useful if you're restricting yourself to consider models which are linear in the parameters to be estimated. Stated another way, the lasso does not evaluate whether you have chosen the correct form of the relationship between the independent and dependent variable(s).
It is very plausible that there may b... | What are disadvantages of using the lasso for variable selection for regression? | Lasso is only useful if you're restricting yourself to consider models which are linear in the parameters to be estimated. Stated another way, the lasso does not evaluate whether you have chosen the c | What are disadvantages of using the lasso for variable selection for regression?
Lasso is only useful if you're restricting yourself to consider models which are linear in the parameters to be estimated. Stated another way, the lasso does not evaluate whether you have chosen the correct form of the relationship between... | What are disadvantages of using the lasso for variable selection for regression?
Lasso is only useful if you're restricting yourself to consider models which are linear in the parameters to be estimated. Stated another way, the lasso does not evaluate whether you have chosen the c |
9,364 | What are disadvantages of using the lasso for variable selection for regression? | One practical disadvantage of lasso and other regularization techniques is finding the optimal regularization coefficient, lambda. Using cross validation to find this value can be just as expensive as stepwise selection techniques. | What are disadvantages of using the lasso for variable selection for regression? | One practical disadvantage of lasso and other regularization techniques is finding the optimal regularization coefficient, lambda. Using cross validation to find this value can be just as expensive as | What are disadvantages of using the lasso for variable selection for regression?
One practical disadvantage of lasso and other regularization techniques is finding the optimal regularization coefficient, lambda. Using cross validation to find this value can be just as expensive as stepwise selection techniques. | What are disadvantages of using the lasso for variable selection for regression?
One practical disadvantage of lasso and other regularization techniques is finding the optimal regularization coefficient, lambda. Using cross validation to find this value can be just as expensive as |
9,365 | What are disadvantages of using the lasso for variable selection for regression? | I am not a LASSO expert but I am an expert in time series. If you have time series data or spatial data then I would studiously avoid a solution that was predicated on independent observations. Furthermore if there are unknown deterministic effects that have played havoc with your data (level shifts / time trends etc) ... | What are disadvantages of using the lasso for variable selection for regression? | I am not a LASSO expert but I am an expert in time series. If you have time series data or spatial data then I would studiously avoid a solution that was predicated on independent observations. Furthe | What are disadvantages of using the lasso for variable selection for regression?
I am not a LASSO expert but I am an expert in time series. If you have time series data or spatial data then I would studiously avoid a solution that was predicated on independent observations. Furthermore if there are unknown deterministi... | What are disadvantages of using the lasso for variable selection for regression?
I am not a LASSO expert but I am an expert in time series. If you have time series data or spatial data then I would studiously avoid a solution that was predicated on independent observations. Furthe |
9,366 | What are disadvantages of using the lasso for variable selection for regression? | One big one is the difficulty of doing hypothesis testing. You can't easily figure out which variables are statistically significant with Lasso. With stepwise regression, you can do hypothesis testing to some degree, if you're careful about your treatment of multiple testing. | What are disadvantages of using the lasso for variable selection for regression? | One big one is the difficulty of doing hypothesis testing. You can't easily figure out which variables are statistically significant with Lasso. With stepwise regression, you can do hypothesis testi | What are disadvantages of using the lasso for variable selection for regression?
One big one is the difficulty of doing hypothesis testing. You can't easily figure out which variables are statistically significant with Lasso. With stepwise regression, you can do hypothesis testing to some degree, if you're careful ab... | What are disadvantages of using the lasso for variable selection for regression?
One big one is the difficulty of doing hypothesis testing. You can't easily figure out which variables are statistically significant with Lasso. With stepwise regression, you can do hypothesis testi |
9,367 | What are disadvantages of using the lasso for variable selection for regression? | I have always found variable reduction techniques hurting the predictability, especially for multi classification. Stepwise elimination methods are also not very effective with highly correlated predictors, they are time consuming too. It is a tough area to deal with and it should be done differently on case to case ba... | What are disadvantages of using the lasso for variable selection for regression? | I have always found variable reduction techniques hurting the predictability, especially for multi classification. Stepwise elimination methods are also not very effective with highly correlated predi | What are disadvantages of using the lasso for variable selection for regression?
I have always found variable reduction techniques hurting the predictability, especially for multi classification. Stepwise elimination methods are also not very effective with highly correlated predictors, they are time consuming too. It ... | What are disadvantages of using the lasso for variable selection for regression?
I have always found variable reduction techniques hurting the predictability, especially for multi classification. Stepwise elimination methods are also not very effective with highly correlated predi |
9,368 | What are disadvantages of using the lasso for variable selection for regression? | There is a simple reason why not using LASSO for variable selection. It just does not work as well as advertised. This is due to its fitting algorithm that includes a penalty factor that penalizes the model against higher regression coefficients. It seems like a good idea, as people think it always reduces model ove... | What are disadvantages of using the lasso for variable selection for regression? | There is a simple reason why not using LASSO for variable selection. It just does not work as well as advertised. This is due to its fitting algorithm that includes a penalty factor that penalizes t | What are disadvantages of using the lasso for variable selection for regression?
There is a simple reason why not using LASSO for variable selection. It just does not work as well as advertised. This is due to its fitting algorithm that includes a penalty factor that penalizes the model against higher regression coef... | What are disadvantages of using the lasso for variable selection for regression?
There is a simple reason why not using LASSO for variable selection. It just does not work as well as advertised. This is due to its fitting algorithm that includes a penalty factor that penalizes t |
9,369 | What important ideas came since Nelder and McCullagh's book Generalized Linear Models (a 40 year old book)? | Your premise that the elapsing of 40 years means that "surely things have changed" is quite dubious in a field relating to applied mathematics. In mathematical work it is often the case that early research on a model form provides all of its essential properties and theory pretty well, and then subsequent research mak... | What important ideas came since Nelder and McCullagh's book Generalized Linear Models (a 40 year old | Your premise that the elapsing of 40 years means that "surely things have changed" is quite dubious in a field relating to applied mathematics. In mathematical work it is often the case that early re | What important ideas came since Nelder and McCullagh's book Generalized Linear Models (a 40 year old book)?
Your premise that the elapsing of 40 years means that "surely things have changed" is quite dubious in a field relating to applied mathematics. In mathematical work it is often the case that early research on a ... | What important ideas came since Nelder and McCullagh's book Generalized Linear Models (a 40 year old
Your premise that the elapsing of 40 years means that "surely things have changed" is quite dubious in a field relating to applied mathematics. In mathematical work it is often the case that early re |
9,370 | What important ideas came since Nelder and McCullagh's book Generalized Linear Models (a 40 year old book)? | In addition to Ben's great answer (+1):
Penalised regression models ($L_1$, $L_2$, elastic net, SCAD (Smoothly clipped absolute deviation), LARS (least-angle regression), MCP (Multiple Change Points), PCR (Principal Component Regression), PLS (Partial least squares) etc.) really came to the fore and became especially... | What important ideas came since Nelder and McCullagh's book Generalized Linear Models (a 40 year old | In addition to Ben's great answer (+1):
Penalised regression models ($L_1$, $L_2$, elastic net, SCAD (Smoothly clipped absolute deviation), LARS (least-angle regression), MCP (Multiple Change Points) | What important ideas came since Nelder and McCullagh's book Generalized Linear Models (a 40 year old book)?
In addition to Ben's great answer (+1):
Penalised regression models ($L_1$, $L_2$, elastic net, SCAD (Smoothly clipped absolute deviation), LARS (least-angle regression), MCP (Multiple Change Points), PCR (Princ... | What important ideas came since Nelder and McCullagh's book Generalized Linear Models (a 40 year old
In addition to Ben's great answer (+1):
Penalised regression models ($L_1$, $L_2$, elastic net, SCAD (Smoothly clipped absolute deviation), LARS (least-angle regression), MCP (Multiple Change Points) |
9,371 | What important ideas came since Nelder and McCullagh's book Generalized Linear Models (a 40 year old book)? | To add to Ben's great answer, since Nelder and McCullagh's GLMs (1983) is mentioned, I think it's fair to say that one important development after that was the extension of the GLM idea to other families of distributions, that generalize the single parameter exponential family.
Important examples include both Beta and ... | What important ideas came since Nelder and McCullagh's book Generalized Linear Models (a 40 year old | To add to Ben's great answer, since Nelder and McCullagh's GLMs (1983) is mentioned, I think it's fair to say that one important development after that was the extension of the GLM idea to other famil | What important ideas came since Nelder and McCullagh's book Generalized Linear Models (a 40 year old book)?
To add to Ben's great answer, since Nelder and McCullagh's GLMs (1983) is mentioned, I think it's fair to say that one important development after that was the extension of the GLM idea to other families of distr... | What important ideas came since Nelder and McCullagh's book Generalized Linear Models (a 40 year old
To add to Ben's great answer, since Nelder and McCullagh's GLMs (1983) is mentioned, I think it's fair to say that one important development after that was the extension of the GLM idea to other famil |
9,372 | What important ideas came since Nelder and McCullagh's book Generalized Linear Models (a 40 year old book)? | Hence, what would be the most important developments since Nelder and McCullagh's book came out regarding GLM theory and application? What am I missing from just reading that book? How should I supplement my knowledge?
Numerical methods if you want to implement the theory on your own.
Computers are now more powerful a... | What important ideas came since Nelder and McCullagh's book Generalized Linear Models (a 40 year old | Hence, what would be the most important developments since Nelder and McCullagh's book came out regarding GLM theory and application? What am I missing from just reading that book? How should I supple | What important ideas came since Nelder and McCullagh's book Generalized Linear Models (a 40 year old book)?
Hence, what would be the most important developments since Nelder and McCullagh's book came out regarding GLM theory and application? What am I missing from just reading that book? How should I supplement my know... | What important ideas came since Nelder and McCullagh's book Generalized Linear Models (a 40 year old
Hence, what would be the most important developments since Nelder and McCullagh's book came out regarding GLM theory and application? What am I missing from just reading that book? How should I supple |
9,373 | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features data | Obviously, k-means needs to be able to compute means.
However, there is a well-known variation of it known as k-medoids or PAM (Partitioning Around Medoids), where the medoid is the existing object most central to the cluster. K-medoids only needs the pairwise distances. | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features da | Obviously, k-means needs to be able to compute means.
However, there is a well-known variation of it known as k-medoids or PAM (Partitioning Around Medoids), where the medoid is the existing object mo | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features data
Obviously, k-means needs to be able to compute means.
However, there is a well-known variation of it known as k-medoids or PAM (Partitioning Around Medoids), where the medoid is the existing object most central to the... | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features da
Obviously, k-means needs to be able to compute means.
However, there is a well-known variation of it known as k-medoids or PAM (Partitioning Around Medoids), where the medoid is the existing object mo |
9,374 | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features data | You are exactly describing the problem setting of kernel $k$-means; when you cannot represent a data point as a Euclidean vector, but if you can still calculate (or define) the inner product between two data points then you can kernelize the algorithm. The following webpage provides brief description of the algorithm:
... | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features da | You are exactly describing the problem setting of kernel $k$-means; when you cannot represent a data point as a Euclidean vector, but if you can still calculate (or define) the inner product between t | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features data
You are exactly describing the problem setting of kernel $k$-means; when you cannot represent a data point as a Euclidean vector, but if you can still calculate (or define) the inner product between two data points th... | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features da
You are exactly describing the problem setting of kernel $k$-means; when you cannot represent a data point as a Euclidean vector, but if you can still calculate (or define) the inner product between t |
9,375 | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features data | @gung is absolutely correct suggesting you multidimensional scaling (MDS) as a preliminary tool to create points X dimensions data out of distance matrix. I'm to add just few strokes. K-means clustering implies euclidean distances. MDS will give you points-in-dimensions coordinates thereby guaranteeing you euclidean di... | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features da | @gung is absolutely correct suggesting you multidimensional scaling (MDS) as a preliminary tool to create points X dimensions data out of distance matrix. I'm to add just few strokes. K-means clusteri | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features data
@gung is absolutely correct suggesting you multidimensional scaling (MDS) as a preliminary tool to create points X dimensions data out of distance matrix. I'm to add just few strokes. K-means clustering implies euclid... | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features da
@gung is absolutely correct suggesting you multidimensional scaling (MDS) as a preliminary tool to create points X dimensions data out of distance matrix. I'm to add just few strokes. K-means clusteri |
9,376 | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features data | I certainly don't know how it's "normally" done, and for the record, I don't know much about cluster analysis. However, are you familiar with Multidimensional Scaling? (Here's another reference, the wiki, and you could search CV under the multidimensional-scaling tag.) Multidimensional scaling takes in a matrix of p... | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features da | I certainly don't know how it's "normally" done, and for the record, I don't know much about cluster analysis. However, are you familiar with Multidimensional Scaling? (Here's another reference, the | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features data
I certainly don't know how it's "normally" done, and for the record, I don't know much about cluster analysis. However, are you familiar with Multidimensional Scaling? (Here's another reference, the wiki, and you co... | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features da
I certainly don't know how it's "normally" done, and for the record, I don't know much about cluster analysis. However, are you familiar with Multidimensional Scaling? (Here's another reference, the |
9,377 | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features data | Optimal Cluster Preserving Embedding of Nonmetric Proximity Data should fit your case. The paper shows how you can obtain a metric vector representation of your objects given only a matrix of pairwise dissimilarity function such that the cluster assignments will be preserved for a range of clustering algorithms, includ... | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features da | Optimal Cluster Preserving Embedding of Nonmetric Proximity Data should fit your case. The paper shows how you can obtain a metric vector representation of your objects given only a matrix of pairwise | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features data
Optimal Cluster Preserving Embedding of Nonmetric Proximity Data should fit your case. The paper shows how you can obtain a metric vector representation of your objects given only a matrix of pairwise dissimilarity fu... | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features da
Optimal Cluster Preserving Embedding of Nonmetric Proximity Data should fit your case. The paper shows how you can obtain a metric vector representation of your objects given only a matrix of pairwise |
9,378 | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features data | Your data can also be viewed as a network, and you can use one of the many network clustering algorithms available. For this you would probably need to apply a threshold on the edge weights, and transform distances to similarities. It is not the 'statistics' way of doing things, but cluster analysis is an underspecifie... | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features da | Your data can also be viewed as a network, and you can use one of the many network clustering algorithms available. For this you would probably need to apply a threshold on the edge weights, and trans | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features data
Your data can also be viewed as a network, and you can use one of the many network clustering algorithms available. For this you would probably need to apply a threshold on the edge weights, and transform distances to... | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features da
Your data can also be viewed as a network, and you can use one of the many network clustering algorithms available. For this you would probably need to apply a threshold on the edge weights, and trans |
9,379 | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features data | I don't know why it is so uncommon in literature, however the solution suggested by @gung and @ttnphns (first projecting your pairwise distances into a Euclidean space using Principal Coordinates Analysis, for example through this package if you use R, and then doing K-means usual way) is simple and doesn't require spe... | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features da | I don't know why it is so uncommon in literature, however the solution suggested by @gung and @ttnphns (first projecting your pairwise distances into a Euclidean space using Principal Coordinates Anal | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features data
I don't know why it is so uncommon in literature, however the solution suggested by @gung and @ttnphns (first projecting your pairwise distances into a Euclidean space using Principal Coordinates Analysis, for example... | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features da
I don't know why it is so uncommon in literature, however the solution suggested by @gung and @ttnphns (first projecting your pairwise distances into a Euclidean space using Principal Coordinates Anal |
9,380 | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features data | With regard to clustering and MDS I would suggest the following resources:
Numerical Ecology (Pierre Legendre and Louis Legendre): http://www.sciencedirect.com/science/bookseries/01678892/20 or http://tinyurl.com/cgrdfqk (google books)
-- chapter on 'Ecological resemblance'
Cluster Analysis (Brian S. Everitt, Sabine L... | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features da | With regard to clustering and MDS I would suggest the following resources:
Numerical Ecology (Pierre Legendre and Louis Legendre): http://www.sciencedirect.com/science/bookseries/01678892/20 or http: | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features data
With regard to clustering and MDS I would suggest the following resources:
Numerical Ecology (Pierre Legendre and Louis Legendre): http://www.sciencedirect.com/science/bookseries/01678892/20 or http://tinyurl.com/cgr... | Perform K-means (or its close kin) clustering with only a distance matrix, not points-by-features da
With regard to clustering and MDS I would suggest the following resources:
Numerical Ecology (Pierre Legendre and Louis Legendre): http://www.sciencedirect.com/science/bookseries/01678892/20 or http: |
9,381 | Is there any theory or field of study that concerns itself with modeling causation rather than correlation? | There are two main approaches to the New Causal Revolution. One is the graphical approach (as in, directed acyclic graphs), championed by Judea Pearl. The other is the potential outcomes framework, championed by Donald Rubin.
For the graphical approach, I recommend these books in this order:
The Book of Why, by Pearl ... | Is there any theory or field of study that concerns itself with modeling causation rather than corre | There are two main approaches to the New Causal Revolution. One is the graphical approach (as in, directed acyclic graphs), championed by Judea Pearl. The other is the potential outcomes framework, ch | Is there any theory or field of study that concerns itself with modeling causation rather than correlation?
There are two main approaches to the New Causal Revolution. One is the graphical approach (as in, directed acyclic graphs), championed by Judea Pearl. The other is the potential outcomes framework, championed by ... | Is there any theory or field of study that concerns itself with modeling causation rather than corre
There are two main approaches to the New Causal Revolution. One is the graphical approach (as in, directed acyclic graphs), championed by Judea Pearl. The other is the potential outcomes framework, ch |
9,382 | Is there any theory or field of study that concerns itself with modeling causation rather than correlation? | Causal inference is a part of statistical inference, so it falls within the field of statistics. Causal inference generally requires inference of statistical associations under an appropriate experimental structure that limits statistical associations to certain structures. This is dealt with in specialist books that... | Is there any theory or field of study that concerns itself with modeling causation rather than corre | Causal inference is a part of statistical inference, so it falls within the field of statistics. Causal inference generally requires inference of statistical associations under an appropriate experim | Is there any theory or field of study that concerns itself with modeling causation rather than correlation?
Causal inference is a part of statistical inference, so it falls within the field of statistics. Causal inference generally requires inference of statistical associations under an appropriate experimental struct... | Is there any theory or field of study that concerns itself with modeling causation rather than corre
Causal inference is a part of statistical inference, so it falls within the field of statistics. Causal inference generally requires inference of statistical associations under an appropriate experim |
9,383 | Is there any theory or field of study that concerns itself with modeling causation rather than correlation? | Adrian Keister provided a great answer. My answer continues his. It took me a while to realize that the 2 different approaches to causal inference (graphical approach and potential outcomes) are complementary. To get the best appreciation for how these 2 approaches to causal inference work together I would recommend re... | Is there any theory or field of study that concerns itself with modeling causation rather than corre | Adrian Keister provided a great answer. My answer continues his. It took me a while to realize that the 2 different approaches to causal inference (graphical approach and potential outcomes) are compl | Is there any theory or field of study that concerns itself with modeling causation rather than correlation?
Adrian Keister provided a great answer. My answer continues his. It took me a while to realize that the 2 different approaches to causal inference (graphical approach and potential outcomes) are complementary. To... | Is there any theory or field of study that concerns itself with modeling causation rather than corre
Adrian Keister provided a great answer. My answer continues his. It took me a while to realize that the 2 different approaches to causal inference (graphical approach and potential outcomes) are compl |
9,384 | Is there any theory or field of study that concerns itself with modeling causation rather than correlation? | Simple answer. Yes - the scientific method is there to help FIND and confirm causal relationships. It requires an hypothesis, then a controlled experiment, then a (statistically verified) conclusion. As data are gathered to support the hypothesis, it becomes more and more certain that "x" causes "y". | Is there any theory or field of study that concerns itself with modeling causation rather than corre | Simple answer. Yes - the scientific method is there to help FIND and confirm causal relationships. It requires an hypothesis, then a controlled experiment, then a (statistically verified) conclusion. | Is there any theory or field of study that concerns itself with modeling causation rather than correlation?
Simple answer. Yes - the scientific method is there to help FIND and confirm causal relationships. It requires an hypothesis, then a controlled experiment, then a (statistically verified) conclusion. As data are ... | Is there any theory or field of study that concerns itself with modeling causation rather than corre
Simple answer. Yes - the scientific method is there to help FIND and confirm causal relationships. It requires an hypothesis, then a controlled experiment, then a (statistically verified) conclusion. |
9,385 | Book recommendations for multivariate analysis | Off the top of my head, I would say that the following general purpose books are rather interesting as a first start:
Izenman, J. Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning. Springer. companion website
Tinsley, H. and Brown, S. (2000). Handbook of Applied Multivariate... | Book recommendations for multivariate analysis | Off the top of my head, I would say that the following general purpose books are rather interesting as a first start:
Izenman, J. Modern Multivariate Statistical Techniques: Regression, Classificatio | Book recommendations for multivariate analysis
Off the top of my head, I would say that the following general purpose books are rather interesting as a first start:
Izenman, J. Modern Multivariate Statistical Techniques: Regression, Classification, and Manifold Learning. Springer. companion website
Tinsley, H. and Bro... | Book recommendations for multivariate analysis
Off the top of my head, I would say that the following general purpose books are rather interesting as a first start:
Izenman, J. Modern Multivariate Statistical Techniques: Regression, Classificatio |
9,386 | Book recommendations for multivariate analysis | Almost the same question was asked recently on the ISOSTAT listserver (frequented by college professors):
If you had a strong undergraduate student who was interested in learning about various multivariate methods (e.g. PCA, MANOVA, discriminant analysis, ...) is there a good, accessible book you might recommend she/h... | Book recommendations for multivariate analysis | Almost the same question was asked recently on the ISOSTAT listserver (frequented by college professors):
If you had a strong undergraduate student who was interested in learning about various multiv | Book recommendations for multivariate analysis
Almost the same question was asked recently on the ISOSTAT listserver (frequented by college professors):
If you had a strong undergraduate student who was interested in learning about various multivariate methods (e.g. PCA, MANOVA, discriminant analysis, ...) is there a ... | Book recommendations for multivariate analysis
Almost the same question was asked recently on the ISOSTAT listserver (frequented by college professors):
If you had a strong undergraduate student who was interested in learning about various multiv |
9,387 | Book recommendations for multivariate analysis | Here are some of my books on that field (in alphabetical order).
AFIFI, A., CLARK, V. Computer-Aided
Multivariate Analysis. CHAPMAN & HALL, 2000
AGRESTI, A. Categorical Data Analysis. WILEY, 2002
HAIR, Multivariate Data Analysis. 6th Ed.
ΗÄRDLE, W., SIMAR, L. Applied Multivariate Statistical Analysis. SPRINGER, 2007.... | Book recommendations for multivariate analysis | Here are some of my books on that field (in alphabetical order).
AFIFI, A., CLARK, V. Computer-Aided
Multivariate Analysis. CHAPMAN & HALL, 2000
AGRESTI, A. Categorical Data Analysis. WILEY, 2002
HA | Book recommendations for multivariate analysis
Here are some of my books on that field (in alphabetical order).
AFIFI, A., CLARK, V. Computer-Aided
Multivariate Analysis. CHAPMAN & HALL, 2000
AGRESTI, A. Categorical Data Analysis. WILEY, 2002
HAIR, Multivariate Data Analysis. 6th Ed.
ΗÄRDLE, W., SIMAR, L. Applied Mul... | Book recommendations for multivariate analysis
Here are some of my books on that field (in alphabetical order).
AFIFI, A., CLARK, V. Computer-Aided
Multivariate Analysis. CHAPMAN & HALL, 2000
AGRESTI, A. Categorical Data Analysis. WILEY, 2002
HA |
9,388 | Book recommendations for multivariate analysis | JOHNSON R., WICHERN D., Applied Multivariate Statistical Analysis, is what we used in our undergraduate Multivariate class at UC Davis, and it does a pretty good job (though it's a bit pricey). | Book recommendations for multivariate analysis | JOHNSON R., WICHERN D., Applied Multivariate Statistical Analysis, is what we used in our undergraduate Multivariate class at UC Davis, and it does a pretty good job (though it's a bit pricey). | Book recommendations for multivariate analysis
JOHNSON R., WICHERN D., Applied Multivariate Statistical Analysis, is what we used in our undergraduate Multivariate class at UC Davis, and it does a pretty good job (though it's a bit pricey). | Book recommendations for multivariate analysis
JOHNSON R., WICHERN D., Applied Multivariate Statistical Analysis, is what we used in our undergraduate Multivariate class at UC Davis, and it does a pretty good job (though it's a bit pricey). |
9,389 | Book recommendations for multivariate analysis | "An Introduction to Multivariate Statistical Analysis" Third edition by T. W. Anderson .
Wiley series in Probability and Statistics. | Book recommendations for multivariate analysis | "An Introduction to Multivariate Statistical Analysis" Third edition by T. W. Anderson .
Wiley series in Probability and Statistics. | Book recommendations for multivariate analysis
"An Introduction to Multivariate Statistical Analysis" Third edition by T. W. Anderson .
Wiley series in Probability and Statistics. | Book recommendations for multivariate analysis
"An Introduction to Multivariate Statistical Analysis" Third edition by T. W. Anderson .
Wiley series in Probability and Statistics. |
9,390 | Book recommendations for multivariate analysis | Hands down best basic text on multivariate regression is (still) Cohen, J., Cohen, P., West, S.G. & Aiken, L.S. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, (L. Erlbaum Associates, Mahwah, N.J., 2003).
Cohen made his name in statistics yet was a psychologist; still if you want social ps... | Book recommendations for multivariate analysis | Hands down best basic text on multivariate regression is (still) Cohen, J., Cohen, P., West, S.G. & Aiken, L.S. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, (L. Erlbau | Book recommendations for multivariate analysis
Hands down best basic text on multivariate regression is (still) Cohen, J., Cohen, P., West, S.G. & Aiken, L.S. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, (L. Erlbaum Associates, Mahwah, N.J., 2003).
Cohen made his name in statistics yet ... | Book recommendations for multivariate analysis
Hands down best basic text on multivariate regression is (still) Cohen, J., Cohen, P., West, S.G. & Aiken, L.S. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences, (L. Erlbau |
9,391 | Book recommendations for multivariate analysis | Analyzing Multivariate Data by James Lattin, J Douglas Carroll and Paul E Green. | Book recommendations for multivariate analysis | Analyzing Multivariate Data by James Lattin, J Douglas Carroll and Paul E Green. | Book recommendations for multivariate analysis
Analyzing Multivariate Data by James Lattin, J Douglas Carroll and Paul E Green. | Book recommendations for multivariate analysis
Analyzing Multivariate Data by James Lattin, J Douglas Carroll and Paul E Green. |
9,392 | Book recommendations for multivariate analysis | Tabachnick is the most cited on Google Scholar
Hair (6th ed) has the most ratings (with a score above 4.5) on Amazon
I recommend Hair, as I've read it, and it is written in plain language.
If you are a student or staff at a university, then I would see if your school has an account with SpringerLink, as the Hardle boo... | Book recommendations for multivariate analysis | Tabachnick is the most cited on Google Scholar
Hair (6th ed) has the most ratings (with a score above 4.5) on Amazon
I recommend Hair, as I've read it, and it is written in plain language.
If you are | Book recommendations for multivariate analysis
Tabachnick is the most cited on Google Scholar
Hair (6th ed) has the most ratings (with a score above 4.5) on Amazon
I recommend Hair, as I've read it, and it is written in plain language.
If you are a student or staff at a university, then I would see if your school has ... | Book recommendations for multivariate analysis
Tabachnick is the most cited on Google Scholar
Hair (6th ed) has the most ratings (with a score above 4.5) on Amazon
I recommend Hair, as I've read it, and it is written in plain language.
If you are |
9,393 | Book recommendations for multivariate analysis | Hastie, T., Tibshirani, R. and Friedman, J.: "The Elements of Statistical Learning: Data Mining, Inference, and Prediction.", Springer (book home page) | Book recommendations for multivariate analysis | Hastie, T., Tibshirani, R. and Friedman, J.: "The Elements of Statistical Learning: Data Mining, Inference, and Prediction.", Springer (book home page) | Book recommendations for multivariate analysis
Hastie, T., Tibshirani, R. and Friedman, J.: "The Elements of Statistical Learning: Data Mining, Inference, and Prediction.", Springer (book home page) | Book recommendations for multivariate analysis
Hastie, T., Tibshirani, R. and Friedman, J.: "The Elements of Statistical Learning: Data Mining, Inference, and Prediction.", Springer (book home page) |
9,394 | Book recommendations for multivariate analysis | If you look at Paul Hewison's webpage, you can find his free book on Multivariate Statistics and R. Another free book is by Wolfgang Hardle and Leopold Simar. I have been
working my way through Johnson and Wichern, a book that has been used in the US for
over twenty years; you will have to buy this book. | Book recommendations for multivariate analysis | If you look at Paul Hewison's webpage, you can find his free book on Multivariate Statistics and R. Another free book is by Wolfgang Hardle and Leopold Simar. I have been
working my way through Johnso | Book recommendations for multivariate analysis
If you look at Paul Hewison's webpage, you can find his free book on Multivariate Statistics and R. Another free book is by Wolfgang Hardle and Leopold Simar. I have been
working my way through Johnson and Wichern, a book that has been used in the US for
over twenty years;... | Book recommendations for multivariate analysis
If you look at Paul Hewison's webpage, you can find his free book on Multivariate Statistics and R. Another free book is by Wolfgang Hardle and Leopold Simar. I have been
working my way through Johnso |
9,395 | Book recommendations for multivariate analysis | One of my favorite one is Legendre & Legendre (2012). Numerical Ecology, 3rd edition.
They cover many statistical analyses and their information on multivariate analyses is particularly excellent. In addition they discuss R packages they created. Definitively a must!
Another excellent one is Quinn & Keough (2002) Exper... | Book recommendations for multivariate analysis | One of my favorite one is Legendre & Legendre (2012). Numerical Ecology, 3rd edition.
They cover many statistical analyses and their information on multivariate analyses is particularly excellent. In | Book recommendations for multivariate analysis
One of my favorite one is Legendre & Legendre (2012). Numerical Ecology, 3rd edition.
They cover many statistical analyses and their information on multivariate analyses is particularly excellent. In addition they discuss R packages they created. Definitively a must!
Anoth... | Book recommendations for multivariate analysis
One of my favorite one is Legendre & Legendre (2012). Numerical Ecology, 3rd edition.
They cover many statistical analyses and their information on multivariate analyses is particularly excellent. In |
9,396 | Can mean be less than half of median if all numbers are non-negative? | You are correct. This is one example of a general result called Markov's inequality, which says that for a non-negative random variable $X$ and number $a$,
$$P(X\geq a)\leq \frac{E[X]}{a}$$
If you plug in the median of $X$ for $a$ you get
$$P(X\geq \text{median})\leq \frac{E[X]}{\text{median}}$$
so
$$0.5\leq \frac{E[X... | Can mean be less than half of median if all numbers are non-negative? | You are correct. This is one example of a general result called Markov's inequality, which says that for a non-negative random variable $X$ and number $a$,
$$P(X\geq a)\leq \frac{E[X]}{a}$$
If you pl | Can mean be less than half of median if all numbers are non-negative?
You are correct. This is one example of a general result called Markov's inequality, which says that for a non-negative random variable $X$ and number $a$,
$$P(X\geq a)\leq \frac{E[X]}{a}$$
If you plug in the median of $X$ for $a$ you get
$$P(X\geq ... | Can mean be less than half of median if all numbers are non-negative?
You are correct. This is one example of a general result called Markov's inequality, which says that for a non-negative random variable $X$ and number $a$,
$$P(X\geq a)\leq \frac{E[X]}{a}$$
If you pl |
9,397 | Can mean be less than half of median if all numbers are non-negative? | in a set of non-negative numbers, is it possible for the mean to be less than half of the median?
No. In fact, the mean cannot even be equal to half of the median (except if every value in the set is $0$).
the lowest possible mean is more than one half of the median
This is correct (again, assuming that not all valu... | Can mean be less than half of median if all numbers are non-negative? | in a set of non-negative numbers, is it possible for the mean to be less than half of the median?
No. In fact, the mean cannot even be equal to half of the median (except if every value in the set is | Can mean be less than half of median if all numbers are non-negative?
in a set of non-negative numbers, is it possible for the mean to be less than half of the median?
No. In fact, the mean cannot even be equal to half of the median (except if every value in the set is $0$).
the lowest possible mean is more than one ... | Can mean be less than half of median if all numbers are non-negative?
in a set of non-negative numbers, is it possible for the mean to be less than half of the median?
No. In fact, the mean cannot even be equal to half of the median (except if every value in the set is |
9,398 | Empirical CDF vs CDF | Let $X$ be a random variable.
The cumulative distribution function $F(x)$ gives the $P(X \leq x)$.
An empirical cumulative distribution function function $G(x)$ gives $P(X \leq x)$ based on the observations in your sample.
The distinction is which probability measure is used. For the empirical CDF, you use the probab... | Empirical CDF vs CDF | Let $X$ be a random variable.
The cumulative distribution function $F(x)$ gives the $P(X \leq x)$.
An empirical cumulative distribution function function $G(x)$ gives $P(X \leq x)$ based on the obser | Empirical CDF vs CDF
Let $X$ be a random variable.
The cumulative distribution function $F(x)$ gives the $P(X \leq x)$.
An empirical cumulative distribution function function $G(x)$ gives $P(X \leq x)$ based on the observations in your sample.
The distinction is which probability measure is used. For the empirical CD... | Empirical CDF vs CDF
Let $X$ be a random variable.
The cumulative distribution function $F(x)$ gives the $P(X \leq x)$.
An empirical cumulative distribution function function $G(x)$ gives $P(X \leq x)$ based on the obser |
9,399 | Empirical CDF vs CDF | Is there any difference between Empirical CDF and CDF?
Yes, they're different. An empirical cdf is a proper cdf, but empirical cdfs will always be discrete even when not drawn from a discrete distribution, while the cdf of a distribution can be other things besides discrete.
If you treat a sample as if it were a popu... | Empirical CDF vs CDF | Is there any difference between Empirical CDF and CDF?
Yes, they're different. An empirical cdf is a proper cdf, but empirical cdfs will always be discrete even when not drawn from a discrete distrib | Empirical CDF vs CDF
Is there any difference between Empirical CDF and CDF?
Yes, they're different. An empirical cdf is a proper cdf, but empirical cdfs will always be discrete even when not drawn from a discrete distribution, while the cdf of a distribution can be other things besides discrete.
If you treat a sample... | Empirical CDF vs CDF
Is there any difference between Empirical CDF and CDF?
Yes, they're different. An empirical cdf is a proper cdf, but empirical cdfs will always be discrete even when not drawn from a discrete distrib |
9,400 | Empirical CDF vs CDF | The empirical CDF is built from an actual data set (in the plot below, I used 100 samples from a standard normal distribution). The CDF is a theoretical construct - it is what you would see if you could take infinitely many samples.
The empirical CDF usually approximates the CDF quite well, especially for large samples... | Empirical CDF vs CDF | The empirical CDF is built from an actual data set (in the plot below, I used 100 samples from a standard normal distribution). The CDF is a theoretical construct - it is what you would see if you cou | Empirical CDF vs CDF
The empirical CDF is built from an actual data set (in the plot below, I used 100 samples from a standard normal distribution). The CDF is a theoretical construct - it is what you would see if you could take infinitely many samples.
The empirical CDF usually approximates the CDF quite well, especia... | Empirical CDF vs CDF
The empirical CDF is built from an actual data set (in the plot below, I used 100 samples from a standard normal distribution). The CDF is a theoretical construct - it is what you would see if you cou |
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