idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
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39,201 | Covariance matrix of complex random variables | No a and b from $a+bi$ are not the real and imaginary covariances because there is also a cross term.
Writing out using the definition of complex covariance gives:
$$cov(x_1,x_2)=E[(x_1-\mu_1)(x_2-\mu_2)^\dagger]$$
$$=\frac{1}{N}\sum_{i=1}^N (x_{1,i}-\mu_1)(x_{2,i}-\mu_2)^{\ast}$$
$$=\frac{1}{N}\sum_{i=1}^N (a_{1,i}+... | Covariance matrix of complex random variables | No a and b from $a+bi$ are not the real and imaginary covariances because there is also a cross term.
Writing out using the definition of complex covariance gives:
$$cov(x_1,x_2)=E[(x_1-\mu_1)(x_2-\ | Covariance matrix of complex random variables
No a and b from $a+bi$ are not the real and imaginary covariances because there is also a cross term.
Writing out using the definition of complex covariance gives:
$$cov(x_1,x_2)=E[(x_1-\mu_1)(x_2-\mu_2)^\dagger]$$
$$=\frac{1}{N}\sum_{i=1}^N (x_{1,i}-\mu_1)(x_{2,i}-\mu_2)... | Covariance matrix of complex random variables
No a and b from $a+bi$ are not the real and imaginary covariances because there is also a cross term.
Writing out using the definition of complex covariance gives:
$$cov(x_1,x_2)=E[(x_1-\mu_1)(x_2-\ |
39,202 | Fourier transform in Machine Learning | A couple things come to mind...
Performing convolutions efficiently as products in the Fourier domain. An example would be training large convolutional neural nets.
For example, see: Fast Training of Convolutional Networks through FFTs (Mathieu et al. 2013)
Another application is sparse signal processing, where the goa... | Fourier transform in Machine Learning | A couple things come to mind...
Performing convolutions efficiently as products in the Fourier domain. An example would be training large convolutional neural nets.
For example, see: Fast Training of | Fourier transform in Machine Learning
A couple things come to mind...
Performing convolutions efficiently as products in the Fourier domain. An example would be training large convolutional neural nets.
For example, see: Fast Training of Convolutional Networks through FFTs (Mathieu et al. 2013)
Another application is s... | Fourier transform in Machine Learning
A couple things come to mind...
Performing convolutions efficiently as products in the Fourier domain. An example would be training large convolutional neural nets.
For example, see: Fast Training of |
39,203 | Fourier transform in Machine Learning | In a theory of random processes we use Fourier transform to get the spectral density of a covariance function.
Then spectral density can be used to verify that the function is a covariance function (Bochner-Khinchin's theorem). Also spectral density is useful while proving theoretical results about quality of Gaussian... | Fourier transform in Machine Learning | In a theory of random processes we use Fourier transform to get the spectral density of a covariance function.
Then spectral density can be used to verify that the function is a covariance function ( | Fourier transform in Machine Learning
In a theory of random processes we use Fourier transform to get the spectral density of a covariance function.
Then spectral density can be used to verify that the function is a covariance function (Bochner-Khinchin's theorem). Also spectral density is useful while proving theoret... | Fourier transform in Machine Learning
In a theory of random processes we use Fourier transform to get the spectral density of a covariance function.
Then spectral density can be used to verify that the function is a covariance function ( |
39,204 | Explaining Odds Ratio and Relative Risk to the statistically challenged | You are of course right and it is a common mistake to describe an odds ratio like a relative risk ratio. I would suggest that it would be helpful to propose a more appropriate phrasing to them such as "suggesting that the odds of [participants] [doing X in] [A condition] is 1.90 times higher than in [B condition]." Onc... | Explaining Odds Ratio and Relative Risk to the statistically challenged | You are of course right and it is a common mistake to describe an odds ratio like a relative risk ratio. I would suggest that it would be helpful to propose a more appropriate phrasing to them such as | Explaining Odds Ratio and Relative Risk to the statistically challenged
You are of course right and it is a common mistake to describe an odds ratio like a relative risk ratio. I would suggest that it would be helpful to propose a more appropriate phrasing to them such as "suggesting that the odds of [participants] [do... | Explaining Odds Ratio and Relative Risk to the statistically challenged
You are of course right and it is a common mistake to describe an odds ratio like a relative risk ratio. I would suggest that it would be helpful to propose a more appropriate phrasing to them such as |
39,205 | Explaining Odds Ratio and Relative Risk to the statistically challenged | You are right and they are wrong. This is SUCH a common error that there is a whole genera of papers where the author just catalogues all of the times odds ratios are misinterpreted as changes in likelihood in published papers in a particular field. Here's just one example. Here is a JAMA article discussing the same is... | Explaining Odds Ratio and Relative Risk to the statistically challenged | You are right and they are wrong. This is SUCH a common error that there is a whole genera of papers where the author just catalogues all of the times odds ratios are misinterpreted as changes in like | Explaining Odds Ratio and Relative Risk to the statistically challenged
You are right and they are wrong. This is SUCH a common error that there is a whole genera of papers where the author just catalogues all of the times odds ratios are misinterpreted as changes in likelihood in published papers in a particular field... | Explaining Odds Ratio and Relative Risk to the statistically challenged
You are right and they are wrong. This is SUCH a common error that there is a whole genera of papers where the author just catalogues all of the times odds ratios are misinterpreted as changes in like |
39,206 | Explaining Odds Ratio and Relative Risk to the statistically challenged | I believe you were correct with your response to the authors, but that a source other than Wikipedia would have strengthened your response.
Much has been published about odds vs risk in reliable journals, which would likely be more convincing than the Wikipedia article.
Here is one from 1998 that gives a balanced answe... | Explaining Odds Ratio and Relative Risk to the statistically challenged | I believe you were correct with your response to the authors, but that a source other than Wikipedia would have strengthened your response.
Much has been published about odds vs risk in reliable journ | Explaining Odds Ratio and Relative Risk to the statistically challenged
I believe you were correct with your response to the authors, but that a source other than Wikipedia would have strengthened your response.
Much has been published about odds vs risk in reliable journals, which would likely be more convincing than ... | Explaining Odds Ratio and Relative Risk to the statistically challenged
I believe you were correct with your response to the authors, but that a source other than Wikipedia would have strengthened your response.
Much has been published about odds vs risk in reliable journ |
39,207 | Independence and Order Statistics | Here is a guide to solving this problem (and others like it). I use simulated values to illustrate, so let's begin by simulating a large number of independent realizations from the distribution with density $f$. (All the code in this answer is written in R.)
n <- 4e4 # Number of trials in the simulation
x <- matrix(p... | Independence and Order Statistics | Here is a guide to solving this problem (and others like it). I use simulated values to illustrate, so let's begin by simulating a large number of independent realizations from the distribution with | Independence and Order Statistics
Here is a guide to solving this problem (and others like it). I use simulated values to illustrate, so let's begin by simulating a large number of independent realizations from the distribution with density $f$. (All the code in this answer is written in R.)
n <- 4e4 # Number of tria... | Independence and Order Statistics
Here is a guide to solving this problem (and others like it). I use simulated values to illustrate, so let's begin by simulating a large number of independent realizations from the distribution with |
39,208 | Independence and Order Statistics | Here is an exact symbolic solution which traces out the steps required ... here using automated tools to do the nitty gritties
Let $(X_1, X_2, X_3)$ denote a sample of size 3 from parent pdf $f(x)$:
Then, the joint pdf of the ordered sample $(X_{(1)}, X_{(2)}, X_{(3)})$ is say $g(x_1,x_2,x_3)$:
where I am using the O... | Independence and Order Statistics | Here is an exact symbolic solution which traces out the steps required ... here using automated tools to do the nitty gritties
Let $(X_1, X_2, X_3)$ denote a sample of size 3 from parent pdf $f(x)$:
| Independence and Order Statistics
Here is an exact symbolic solution which traces out the steps required ... here using automated tools to do the nitty gritties
Let $(X_1, X_2, X_3)$ denote a sample of size 3 from parent pdf $f(x)$:
Then, the joint pdf of the ordered sample $(X_{(1)}, X_{(2)}, X_{(3)})$ is say $g(x_1,... | Independence and Order Statistics
Here is an exact symbolic solution which traces out the steps required ... here using automated tools to do the nitty gritties
Let $(X_1, X_2, X_3)$ denote a sample of size 3 from parent pdf $f(x)$:
|
39,209 | Is every ARIMA(1,1,0) model equivalent to an AR(2) model? | The forecast for the ARIMA(1,1,0) enforces the restriction that $d=1$.
It is maybe even easier to see in the AR(1) vs. ARIMA(0,1,0) case: The latter is just
$$
\Delta y_t=\epsilon_t
$$
whose optimal forecasts are 0 at all horizons (we expect $\epsilon_t$ to take the value zero). If we aim to forecast $y_t$ itself, we ... | Is every ARIMA(1,1,0) model equivalent to an AR(2) model? | The forecast for the ARIMA(1,1,0) enforces the restriction that $d=1$.
It is maybe even easier to see in the AR(1) vs. ARIMA(0,1,0) case: The latter is just
$$
\Delta y_t=\epsilon_t
$$
whose optimal | Is every ARIMA(1,1,0) model equivalent to an AR(2) model?
The forecast for the ARIMA(1,1,0) enforces the restriction that $d=1$.
It is maybe even easier to see in the AR(1) vs. ARIMA(0,1,0) case: The latter is just
$$
\Delta y_t=\epsilon_t
$$
whose optimal forecasts are 0 at all horizons (we expect $\epsilon_t$ to tak... | Is every ARIMA(1,1,0) model equivalent to an AR(2) model?
The forecast for the ARIMA(1,1,0) enforces the restriction that $d=1$.
It is maybe even easier to see in the AR(1) vs. ARIMA(0,1,0) case: The latter is just
$$
\Delta y_t=\epsilon_t
$$
whose optimal |
39,210 | Is every ARIMA(1,1,0) model equivalent to an AR(2) model? | The equivalence depends on definitions. General ARMA(p,q) process can be defined as a stochastic process which is the solution to the following equation:
$$ X_t-\phi_1X_{t-1}-...-\phi_pX_{t-p}=Z_t+\theta_1 Z_{t-1}+...+\theta_qZ_{t-q},$$
where $Z_t$ is a white noise process. We must require that polynomials $\phi(z)=1-\... | Is every ARIMA(1,1,0) model equivalent to an AR(2) model? | The equivalence depends on definitions. General ARMA(p,q) process can be defined as a stochastic process which is the solution to the following equation:
$$ X_t-\phi_1X_{t-1}-...-\phi_pX_{t-p}=Z_t+\th | Is every ARIMA(1,1,0) model equivalent to an AR(2) model?
The equivalence depends on definitions. General ARMA(p,q) process can be defined as a stochastic process which is the solution to the following equation:
$$ X_t-\phi_1X_{t-1}-...-\phi_pX_{t-p}=Z_t+\theta_1 Z_{t-1}+...+\theta_qZ_{t-q},$$
where $Z_t$ is a white no... | Is every ARIMA(1,1,0) model equivalent to an AR(2) model?
The equivalence depends on definitions. General ARMA(p,q) process can be defined as a stochastic process which is the solution to the following equation:
$$ X_t-\phi_1X_{t-1}-...-\phi_pX_{t-p}=Z_t+\th |
39,211 | Deterministic clustering approaches | I can point you to an algorithm and a family of algorithms:
The algorithm is called IGMM (Incremental Gaussian Mixture Model). It is robust (but not insensitive) to order. But when data arrives in the same order, it always gives the same result.
A family of clustering algorithms which satisfies your conditions is Spec... | Deterministic clustering approaches | I can point you to an algorithm and a family of algorithms:
The algorithm is called IGMM (Incremental Gaussian Mixture Model). It is robust (but not insensitive) to order. But when data arrives in th | Deterministic clustering approaches
I can point you to an algorithm and a family of algorithms:
The algorithm is called IGMM (Incremental Gaussian Mixture Model). It is robust (but not insensitive) to order. But when data arrives in the same order, it always gives the same result.
A family of clustering algorithms whi... | Deterministic clustering approaches
I can point you to an algorithm and a family of algorithms:
The algorithm is called IGMM (Incremental Gaussian Mixture Model). It is robust (but not insensitive) to order. But when data arrives in th |
39,212 | Deterministic clustering approaches | Hierarchical Agglomerative Clustering is deterministic except for tied distances when not using single-linkage.
DBSCAN is deterministic, except for permutation of the data set in rare cases.
k-means is deterministic except for initialization. You can initialize with the first k objects, then it is deterministic, too.
P... | Deterministic clustering approaches | Hierarchical Agglomerative Clustering is deterministic except for tied distances when not using single-linkage.
DBSCAN is deterministic, except for permutation of the data set in rare cases.
k-means i | Deterministic clustering approaches
Hierarchical Agglomerative Clustering is deterministic except for tied distances when not using single-linkage.
DBSCAN is deterministic, except for permutation of the data set in rare cases.
k-means is deterministic except for initialization. You can initialize with the first k objec... | Deterministic clustering approaches
Hierarchical Agglomerative Clustering is deterministic except for tied distances when not using single-linkage.
DBSCAN is deterministic, except for permutation of the data set in rare cases.
k-means i |
39,213 | Deterministic clustering approaches | All the algorithms, by definition, are deterministic given their inputs. Any algorithm that uses pseudo-random numbers is deterministic given the seed.
K-means, that you used as example, starts with randomly chosen cluster centroids so to find optimal ones. Besides the initialization, the algorithm is totally determini... | Deterministic clustering approaches | All the algorithms, by definition, are deterministic given their inputs. Any algorithm that uses pseudo-random numbers is deterministic given the seed.
K-means, that you used as example, starts with r | Deterministic clustering approaches
All the algorithms, by definition, are deterministic given their inputs. Any algorithm that uses pseudo-random numbers is deterministic given the seed.
K-means, that you used as example, starts with randomly chosen cluster centroids so to find optimal ones. Besides the initialization... | Deterministic clustering approaches
All the algorithms, by definition, are deterministic given their inputs. Any algorithm that uses pseudo-random numbers is deterministic given the seed.
K-means, that you used as example, starts with r |
39,214 | Deterministic clustering approaches | If I were to reinterpret your description of "deterministic," it sounds more like a longitudinal, "confirmatory" cluster analysis to me -- with the important exception that you haven't explicitly integrated time series considerations into your model. Confirmatory clustering methods are deployed once it is felt that the... | Deterministic clustering approaches | If I were to reinterpret your description of "deterministic," it sounds more like a longitudinal, "confirmatory" cluster analysis to me -- with the important exception that you haven't explicitly inte | Deterministic clustering approaches
If I were to reinterpret your description of "deterministic," it sounds more like a longitudinal, "confirmatory" cluster analysis to me -- with the important exception that you haven't explicitly integrated time series considerations into your model. Confirmatory clustering methods a... | Deterministic clustering approaches
If I were to reinterpret your description of "deterministic," it sounds more like a longitudinal, "confirmatory" cluster analysis to me -- with the important exception that you haven't explicitly inte |
39,215 | Is the bootstrap useless in a Bayesian setting? | Bradley Efron has written about this as well as recently participating in a JRSS webinar titled Frequentist Accuracy of Bayesian Estimates (here: http://www.rss.org.uk/RSS/Events/Online_and_virtual_events/Journal_club/Past_Journal_webinars/RSS/Events/Online_and_virtual_events_sub/Past_Journal_webinars.aspx?hkey=5c97f80... | Is the bootstrap useless in a Bayesian setting? | Bradley Efron has written about this as well as recently participating in a JRSS webinar titled Frequentist Accuracy of Bayesian Estimates (here: http://www.rss.org.uk/RSS/Events/Online_and_virtual_ev | Is the bootstrap useless in a Bayesian setting?
Bradley Efron has written about this as well as recently participating in a JRSS webinar titled Frequentist Accuracy of Bayesian Estimates (here: http://www.rss.org.uk/RSS/Events/Online_and_virtual_events/Journal_club/Past_Journal_webinars/RSS/Events/Online_and_virtual_ev... | Is the bootstrap useless in a Bayesian setting?
Bradley Efron has written about this as well as recently participating in a JRSS webinar titled Frequentist Accuracy of Bayesian Estimates (here: http://www.rss.org.uk/RSS/Events/Online_and_virtual_ev |
39,216 | Is the bootstrap useless in a Bayesian setting? | First, your interpretation of Bayesian statistics seems to be a bit restrictive. Bayesian methods do not necessarily rely on belief, e.g. objective Bayesians view the prior as a catalyst needed to express the parameters distribution having observed the data.
Second, when belief is available it is not related to the ob... | Is the bootstrap useless in a Bayesian setting? | First, your interpretation of Bayesian statistics seems to be a bit restrictive. Bayesian methods do not necessarily rely on belief, e.g. objective Bayesians view the prior as a catalyst needed to exp | Is the bootstrap useless in a Bayesian setting?
First, your interpretation of Bayesian statistics seems to be a bit restrictive. Bayesian methods do not necessarily rely on belief, e.g. objective Bayesians view the prior as a catalyst needed to express the parameters distribution having observed the data.
Second, when... | Is the bootstrap useless in a Bayesian setting?
First, your interpretation of Bayesian statistics seems to be a bit restrictive. Bayesian methods do not necessarily rely on belief, e.g. objective Bayesians view the prior as a catalyst needed to exp |
39,217 | What makes neural network a *convolutional* neural network? | Starting from the Neural Network perspective:
I would say that the base Neural Network has all neurons interconnected between layers. The convolutional version simplifies this model using two hypotheses:
meaningful features have a given size in the image.
features are shift equivariant (shifted input leads to similarl... | What makes neural network a *convolutional* neural network? | Starting from the Neural Network perspective:
I would say that the base Neural Network has all neurons interconnected between layers. The convolutional version simplifies this model using two hypothes | What makes neural network a *convolutional* neural network?
Starting from the Neural Network perspective:
I would say that the base Neural Network has all neurons interconnected between layers. The convolutional version simplifies this model using two hypotheses:
meaningful features have a given size in the image.
fea... | What makes neural network a *convolutional* neural network?
Starting from the Neural Network perspective:
I would say that the base Neural Network has all neurons interconnected between layers. The convolutional version simplifies this model using two hypothes |
39,218 | What makes neural network a *convolutional* neural network? | In short, local connectivity and parameter sharing (optional).
In terms of image data,
local connectivity says only neurons within a local region should be connected together, which basically assumes that pixels nearby are correlated, and pixels far apart are independent.
Parameter sharing means that the same set o... | What makes neural network a *convolutional* neural network? | In short, local connectivity and parameter sharing (optional).
In terms of image data,
local connectivity says only neurons within a local region should be connected together, which basically assume | What makes neural network a *convolutional* neural network?
In short, local connectivity and parameter sharing (optional).
In terms of image data,
local connectivity says only neurons within a local region should be connected together, which basically assumes that pixels nearby are correlated, and pixels far apart ar... | What makes neural network a *convolutional* neural network?
In short, local connectivity and parameter sharing (optional).
In terms of image data,
local connectivity says only neurons within a local region should be connected together, which basically assume |
39,219 | What makes neural network a *convolutional* neural network? | The mathematical operation of convolution means to compute the product of two (continuous or discrete) functions over all possible shift-positions. The most simple example is to convolve a 1-dimensional vector ${\bf {\it x}}=(x_1,x_2,x_3,\dots,x_n)^T$ with a sampled Gaussian function (the Gaussian probability density f... | What makes neural network a *convolutional* neural network? | The mathematical operation of convolution means to compute the product of two (continuous or discrete) functions over all possible shift-positions. The most simple example is to convolve a 1-dimension | What makes neural network a *convolutional* neural network?
The mathematical operation of convolution means to compute the product of two (continuous or discrete) functions over all possible shift-positions. The most simple example is to convolve a 1-dimensional vector ${\bf {\it x}}=(x_1,x_2,x_3,\dots,x_n)^T$ with a s... | What makes neural network a *convolutional* neural network?
The mathematical operation of convolution means to compute the product of two (continuous or discrete) functions over all possible shift-positions. The most simple example is to convolve a 1-dimension |
39,220 | Interpreting Standard Deviation of Natural Log Transformed Data | The proposed interpretation in your last paragraph is incorrect -- that increase only applies at the mean. If you started lower, it would be a smaller increase and if you started higher it would be a larger increase.
$e^{a+0.8}- e^a=e^{a}(e^{0.8}- 1)\approx 1.2255 e^a$
It's better to think in terms of percentage increa... | Interpreting Standard Deviation of Natural Log Transformed Data | The proposed interpretation in your last paragraph is incorrect -- that increase only applies at the mean. If you started lower, it would be a smaller increase and if you started higher it would be a | Interpreting Standard Deviation of Natural Log Transformed Data
The proposed interpretation in your last paragraph is incorrect -- that increase only applies at the mean. If you started lower, it would be a smaller increase and if you started higher it would be a larger increase.
$e^{a+0.8}- e^a=e^{a}(e^{0.8}- 1)\appro... | Interpreting Standard Deviation of Natural Log Transformed Data
The proposed interpretation in your last paragraph is incorrect -- that increase only applies at the mean. If you started lower, it would be a smaller increase and if you started higher it would be a |
39,221 | Interpreting Standard Deviation of Natural Log Transformed Data | So a one standard deviation increase of the log-transformed variable translates to 2,706 likes. Is this ok?
You were careful to formulate your statement with 'increase of of log-transformed variable' qualifier. I think this eliminates misunderstanding that could have occurred to a reader who may assume that you're t... | Interpreting Standard Deviation of Natural Log Transformed Data | So a one standard deviation increase of the log-transformed variable translates to 2,706 likes. Is this ok?
You were careful to formulate your statement with 'increase of of log-transformed variabl | Interpreting Standard Deviation of Natural Log Transformed Data
So a one standard deviation increase of the log-transformed variable translates to 2,706 likes. Is this ok?
You were careful to formulate your statement with 'increase of of log-transformed variable' qualifier. I think this eliminates misunderstanding t... | Interpreting Standard Deviation of Natural Log Transformed Data
So a one standard deviation increase of the log-transformed variable translates to 2,706 likes. Is this ok?
You were careful to formulate your statement with 'increase of of log-transformed variabl |
39,222 | Interpreting Standard Deviation of Natural Log Transformed Data | If I understand, you want the standard deviation of Y. The standard deviation of Y is NOT easily calculated from mean(ln(Y)) and sd(ln(Y)), so your formula is not okay. The easy solution is to ignore the log-transform when calculating the standard deviation of Y: i.e. sd(Y) or sd(e^ln(Y)). | Interpreting Standard Deviation of Natural Log Transformed Data | If I understand, you want the standard deviation of Y. The standard deviation of Y is NOT easily calculated from mean(ln(Y)) and sd(ln(Y)), so your formula is not okay. The easy solution is to ignore | Interpreting Standard Deviation of Natural Log Transformed Data
If I understand, you want the standard deviation of Y. The standard deviation of Y is NOT easily calculated from mean(ln(Y)) and sd(ln(Y)), so your formula is not okay. The easy solution is to ignore the log-transform when calculating the standard deviatio... | Interpreting Standard Deviation of Natural Log Transformed Data
If I understand, you want the standard deviation of Y. The standard deviation of Y is NOT easily calculated from mean(ln(Y)) and sd(ln(Y)), so your formula is not okay. The easy solution is to ignore |
39,223 | Online learning in practice | There is no reason to prefer online learning to batch learning when you can use both methods.
But in some cases (millions of features, millions of observations), you have to use online learning because everything else will fail (or will not terminate).
Some implementations of online learning methods even have a consta... | Online learning in practice | There is no reason to prefer online learning to batch learning when you can use both methods.
But in some cases (millions of features, millions of observations), you have to use online learning becaus | Online learning in practice
There is no reason to prefer online learning to batch learning when you can use both methods.
But in some cases (millions of features, millions of observations), you have to use online learning because everything else will fail (or will not terminate).
Some implementations of online learnin... | Online learning in practice
There is no reason to prefer online learning to batch learning when you can use both methods.
But in some cases (millions of features, millions of observations), you have to use online learning becaus |
39,224 | Online learning in practice | There are two main reasons to use online learning in practice:
When not all data fits in memory;
When data arrives in real-time and the amount of processing power per arriving datum is low.
RUser4512 gave a good example of scenario 1 in his answer.
Scenario 2 is very frequently encountered in industrial applications.... | Online learning in practice | There are two main reasons to use online learning in practice:
When not all data fits in memory;
When data arrives in real-time and the amount of processing power per arriving datum is low.
RUser451 | Online learning in practice
There are two main reasons to use online learning in practice:
When not all data fits in memory;
When data arrives in real-time and the amount of processing power per arriving datum is low.
RUser4512 gave a good example of scenario 1 in his answer.
Scenario 2 is very frequently encountered... | Online learning in practice
There are two main reasons to use online learning in practice:
When not all data fits in memory;
When data arrives in real-time and the amount of processing power per arriving datum is low.
RUser451 |
39,225 | Online learning in practice | Online learning is also useful in case of nonstationarities. You might want to track changes in a signal or system. | Online learning in practice | Online learning is also useful in case of nonstationarities. You might want to track changes in a signal or system. | Online learning in practice
Online learning is also useful in case of nonstationarities. You might want to track changes in a signal or system. | Online learning in practice
Online learning is also useful in case of nonstationarities. You might want to track changes in a signal or system. |
39,226 | Percentage of variation in each column explained by each SVD mode | If your singular value decomposition is $$\mathbf X = \mathbf{USV}^\top,$$ then the amount of overall variance explained by the $i$-th pair of SVD vectors ($i$-th SVD "mode") is given by $R^2 = s_i^2/\sum_j s_j^2$, where $s_j$ are singular values (diagonal of $\mathbf S$). This can also be computed as the ratio of the ... | Percentage of variation in each column explained by each SVD mode | If your singular value decomposition is $$\mathbf X = \mathbf{USV}^\top,$$ then the amount of overall variance explained by the $i$-th pair of SVD vectors ($i$-th SVD "mode") is given by $R^2 = s_i^2/ | Percentage of variation in each column explained by each SVD mode
If your singular value decomposition is $$\mathbf X = \mathbf{USV}^\top,$$ then the amount of overall variance explained by the $i$-th pair of SVD vectors ($i$-th SVD "mode") is given by $R^2 = s_i^2/\sum_j s_j^2$, where $s_j$ are singular values (diagon... | Percentage of variation in each column explained by each SVD mode
If your singular value decomposition is $$\mathbf X = \mathbf{USV}^\top,$$ then the amount of overall variance explained by the $i$-th pair of SVD vectors ($i$-th SVD "mode") is given by $R^2 = s_i^2/ |
39,227 | Check whether a coin is fair | [I think I'd start by asking for a whiteboard, markers -- and an eraser, because one boardful isn't enough to explain everything wrong with the question.]
I'm going to answer this question by rejecting its premises.
The "coin" itself is just a coin; by itself it doesn't do anything, and so it cannot be fair or not-fa... | Check whether a coin is fair | [I think I'd start by asking for a whiteboard, markers -- and an eraser, because one boardful isn't enough to explain everything wrong with the question.]
I'm going to answer this question by rejectin | Check whether a coin is fair
[I think I'd start by asking for a whiteboard, markers -- and an eraser, because one boardful isn't enough to explain everything wrong with the question.]
I'm going to answer this question by rejecting its premises.
The "coin" itself is just a coin; by itself it doesn't do anything, and s... | Check whether a coin is fair
[I think I'd start by asking for a whiteboard, markers -- and an eraser, because one boardful isn't enough to explain everything wrong with the question.]
I'm going to answer this question by rejectin |
39,228 | Check whether a coin is fair | I am not going to attempt to provide final answers to your question; I believe the topic is more than addressed after the comprehensive response given by Glen. However, and apropos of his comment about a Bayesian approach, I'd like to post some illustrations about the way our preconceptions about the "fairness" of the ... | Check whether a coin is fair | I am not going to attempt to provide final answers to your question; I believe the topic is more than addressed after the comprehensive response given by Glen. However, and apropos of his comment abou | Check whether a coin is fair
I am not going to attempt to provide final answers to your question; I believe the topic is more than addressed after the comprehensive response given by Glen. However, and apropos of his comment about a Bayesian approach, I'd like to post some illustrations about the way our preconceptions... | Check whether a coin is fair
I am not going to attempt to provide final answers to your question; I believe the topic is more than addressed after the comprehensive response given by Glen. However, and apropos of his comment abou |
39,229 | Check whether a coin is fair | I'm thinking using chi square to measure the statistical difference between categorical variables.
Null hypothesis: half of the coins you tossed are heads and half are tails.
Alternative hypothesis: opposite to the above
Then you calculate the chi square using this formula sum((f0-fe)^2/fe) where f0 is your statistic o... | Check whether a coin is fair | I'm thinking using chi square to measure the statistical difference between categorical variables.
Null hypothesis: half of the coins you tossed are heads and half are tails.
Alternative hypothesis: o | Check whether a coin is fair
I'm thinking using chi square to measure the statistical difference between categorical variables.
Null hypothesis: half of the coins you tossed are heads and half are tails.
Alternative hypothesis: opposite to the above
Then you calculate the chi square using this formula sum((f0-fe)^2/fe)... | Check whether a coin is fair
I'm thinking using chi square to measure the statistical difference between categorical variables.
Null hypothesis: half of the coins you tossed are heads and half are tails.
Alternative hypothesis: o |
39,230 | Implementing Balanced Random Forest (BRF) in R using RandomForests | You can balance your random forests using case weights. Here's a simple example:
library(ranger) #Best random forest implementation in R
#Make a dataste
set.seed(43)
nrow <- 1000
ncol <- 10
X <- matrix(rnorm(nrow * ncol), ncol=ncol)
CF <- rnorm(ncol)
Y <- (X %*% CF + rnorm(nrow))[,1]
Y <- as.integer(Y > quantile(Y, 0... | Implementing Balanced Random Forest (BRF) in R using RandomForests | You can balance your random forests using case weights. Here's a simple example:
library(ranger) #Best random forest implementation in R
#Make a dataste
set.seed(43)
nrow <- 1000
ncol <- 10
X <- mat | Implementing Balanced Random Forest (BRF) in R using RandomForests
You can balance your random forests using case weights. Here's a simple example:
library(ranger) #Best random forest implementation in R
#Make a dataste
set.seed(43)
nrow <- 1000
ncol <- 10
X <- matrix(rnorm(nrow * ncol), ncol=ncol)
CF <- rnorm(ncol)
... | Implementing Balanced Random Forest (BRF) in R using RandomForests
You can balance your random forests using case weights. Here's a simple example:
library(ranger) #Best random forest implementation in R
#Make a dataste
set.seed(43)
nrow <- 1000
ncol <- 10
X <- mat |
39,231 | Implementing Balanced Random Forest (BRF) in R using RandomForests | For reference and adding to @zach's answer:
The package ranger now(*) implements a sample.fraction argument that allows a vector of class-specific values for a stratified sampling scheme suitable for imbalance cases.
(*) see issue #167 and the fix #263 allowing class-wise sample.fraction | Implementing Balanced Random Forest (BRF) in R using RandomForests | For reference and adding to @zach's answer:
The package ranger now(*) implements a sample.fraction argument that allows a vector of class-specific values for a stratified sampling scheme suitable for | Implementing Balanced Random Forest (BRF) in R using RandomForests
For reference and adding to @zach's answer:
The package ranger now(*) implements a sample.fraction argument that allows a vector of class-specific values for a stratified sampling scheme suitable for imbalance cases.
(*) see issue #167 and the fix #263 ... | Implementing Balanced Random Forest (BRF) in R using RandomForests
For reference and adding to @zach's answer:
The package ranger now(*) implements a sample.fraction argument that allows a vector of class-specific values for a stratified sampling scheme suitable for |
39,232 | Implementing Balanced Random Forest (BRF) in R using RandomForests | The writers had a presentation of the techniques found here: http://www.interfacesymposia.org/I04/I2004Proceedings/ChenChao/ChenChao.presentation.pdf
According to the authors, there’s an add-on package to R that implements their original Fortran:
Here are the working links to the R package:
https://cran.r-project.org... | Implementing Balanced Random Forest (BRF) in R using RandomForests | The writers had a presentation of the techniques found here: http://www.interfacesymposia.org/I04/I2004Proceedings/ChenChao/ChenChao.presentation.pdf
According to the authors, there’s an add-on packa | Implementing Balanced Random Forest (BRF) in R using RandomForests
The writers had a presentation of the techniques found here: http://www.interfacesymposia.org/I04/I2004Proceedings/ChenChao/ChenChao.presentation.pdf
According to the authors, there’s an add-on package to R that implements their original Fortran:
Here ... | Implementing Balanced Random Forest (BRF) in R using RandomForests
The writers had a presentation of the techniques found here: http://www.interfacesymposia.org/I04/I2004Proceedings/ChenChao/ChenChao.presentation.pdf
According to the authors, there’s an add-on packa |
39,233 | Implementing Balanced Random Forest (BRF) in R using RandomForests | The "randomForest" function in the "randomForest" R package supports the Balanced Random Forest. One need to specify the "strata" and the "sampsize" parameters to enable the balanced bootstrapping resampling.
strata
A (factor) variable that is used for stratified sampling.
sampsize
Size(s) of sample to draw. For class... | Implementing Balanced Random Forest (BRF) in R using RandomForests | The "randomForest" function in the "randomForest" R package supports the Balanced Random Forest. One need to specify the "strata" and the "sampsize" parameters to enable the balanced bootstrapping res | Implementing Balanced Random Forest (BRF) in R using RandomForests
The "randomForest" function in the "randomForest" R package supports the Balanced Random Forest. One need to specify the "strata" and the "sampsize" parameters to enable the balanced bootstrapping resampling.
strata
A (factor) variable that is used for... | Implementing Balanced Random Forest (BRF) in R using RandomForests
The "randomForest" function in the "randomForest" R package supports the Balanced Random Forest. One need to specify the "strata" and the "sampsize" parameters to enable the balanced bootstrapping res |
39,234 | Choosing the Q-value in the Benjamini-Hochberg procedure to control false discovery rate | Yes, these things are done quite often, at least in genetics. To address your specific points:
This is quite commonly done, and I have personally reported results this way. Though, make sure to make it clear at what level of FDR you are reporting. Think of it akin to "marginal" significance; people may find it interes... | Choosing the Q-value in the Benjamini-Hochberg procedure to control false discovery rate | Yes, these things are done quite often, at least in genetics. To address your specific points:
This is quite commonly done, and I have personally reported results this way. Though, make sure to make | Choosing the Q-value in the Benjamini-Hochberg procedure to control false discovery rate
Yes, these things are done quite often, at least in genetics. To address your specific points:
This is quite commonly done, and I have personally reported results this way. Though, make sure to make it clear at what level of FDR y... | Choosing the Q-value in the Benjamini-Hochberg procedure to control false discovery rate
Yes, these things are done quite often, at least in genetics. To address your specific points:
This is quite commonly done, and I have personally reported results this way. Though, make sure to make |
39,235 | Choosing the Q-value in the Benjamini-Hochberg procedure to control false discovery rate | An alternative you might find useful is to report -- for each comparison -- the q value. The q value (lower case) is the Q value (upper case) at which that particular comparison would be right at the border of being a discovery. You can then report the q value for each comparison, rather than just a list of which compa... | Choosing the Q-value in the Benjamini-Hochberg procedure to control false discovery rate | An alternative you might find useful is to report -- for each comparison -- the q value. The q value (lower case) is the Q value (upper case) at which that particular comparison would be right at the | Choosing the Q-value in the Benjamini-Hochberg procedure to control false discovery rate
An alternative you might find useful is to report -- for each comparison -- the q value. The q value (lower case) is the Q value (upper case) at which that particular comparison would be right at the border of being a discovery. Yo... | Choosing the Q-value in the Benjamini-Hochberg procedure to control false discovery rate
An alternative you might find useful is to report -- for each comparison -- the q value. The q value (lower case) is the Q value (upper case) at which that particular comparison would be right at the |
39,236 | Choosing the Q-value in the Benjamini-Hochberg procedure to control false discovery rate | There's nothing magical about alpha=.05.
I see nothing wrong with going with alpha/q=.10. I would also report confidence intervals (and adjust these as well).
Alternatively, use a Bayesian model with priors up to the job of wacking down false positives (horseshoe, laplace) | Choosing the Q-value in the Benjamini-Hochberg procedure to control false discovery rate | There's nothing magical about alpha=.05.
I see nothing wrong with going with alpha/q=.10. I would also report confidence intervals (and adjust these as well).
Alternatively, use a Bayesian model wit | Choosing the Q-value in the Benjamini-Hochberg procedure to control false discovery rate
There's nothing magical about alpha=.05.
I see nothing wrong with going with alpha/q=.10. I would also report confidence intervals (and adjust these as well).
Alternatively, use a Bayesian model with priors up to the job of wacki... | Choosing the Q-value in the Benjamini-Hochberg procedure to control false discovery rate
There's nothing magical about alpha=.05.
I see nothing wrong with going with alpha/q=.10. I would also report confidence intervals (and adjust these as well).
Alternatively, use a Bayesian model wit |
39,237 | Difference in output between SAS's proc genmod and R's glm | I notice several things here.
First, when you enter your data via matrix, all the data have to be the same type. Thus, they are coerced to be the most inclusive type, strings, which in turn are coerced to be factors by default. Note:
testdata <- data.frame(matrix(c("f","Test", 1.75, 16, 0, 16, 0, 1, 1,
...
s... | Difference in output between SAS's proc genmod and R's glm | I notice several things here.
First, when you enter your data via matrix, all the data have to be the same type. Thus, they are coerced to be the most inclusive type, strings, which in turn are coe | Difference in output between SAS's proc genmod and R's glm
I notice several things here.
First, when you enter your data via matrix, all the data have to be the same type. Thus, they are coerced to be the most inclusive type, strings, which in turn are coerced to be factors by default. Note:
testdata <- data.fram... | Difference in output between SAS's proc genmod and R's glm
I notice several things here.
First, when you enter your data via matrix, all the data have to be the same type. Thus, they are coerced to be the most inclusive type, strings, which in turn are coe |
39,238 | Finding the maximum point of probability density function | It's not a stupid question at all. See this post for a case where a likelihood can have two maxima and a minimum.
When dealing with maximum likelihood in a general theoretical approach, we tend to silently assume that the likelihood is a unimodal function (usually having a maximum). Moreover, many "known" distribution... | Finding the maximum point of probability density function | It's not a stupid question at all. See this post for a case where a likelihood can have two maxima and a minimum.
When dealing with maximum likelihood in a general theoretical approach, we tend to si | Finding the maximum point of probability density function
It's not a stupid question at all. See this post for a case where a likelihood can have two maxima and a minimum.
When dealing with maximum likelihood in a general theoretical approach, we tend to silently assume that the likelihood is a unimodal function (usua... | Finding the maximum point of probability density function
It's not a stupid question at all. See this post for a case where a likelihood can have two maxima and a minimum.
When dealing with maximum likelihood in a general theoretical approach, we tend to si |
39,239 | Finding the maximum point of probability density function | First of all, in response to answer from Alecos Papadopoulos, should the software check for negative definite? Yes? Do they? I suspect many don't. But actually, if there are any constraints, including bound constraints such as parameters being nonnegative, and one or more constraints are "active" at a candidate solut... | Finding the maximum point of probability density function | First of all, in response to answer from Alecos Papadopoulos, should the software check for negative definite? Yes? Do they? I suspect many don't. But actually, if there are any constraints, includi | Finding the maximum point of probability density function
First of all, in response to answer from Alecos Papadopoulos, should the software check for negative definite? Yes? Do they? I suspect many don't. But actually, if there are any constraints, including bound constraints such as parameters being nonnegative, and... | Finding the maximum point of probability density function
First of all, in response to answer from Alecos Papadopoulos, should the software check for negative definite? Yes? Do they? I suspect many don't. But actually, if there are any constraints, includi |
39,240 | Lognormal Regression? | I would suggest using a generalised linear model (GLM) with a log-link function instead of directly log-transforming your variables; in R you can simply use glm with family= gaussian(link='log') to begin with.
I say this because modelling the mean of the log-transformed variable (as you would do by simply taking the l... | Lognormal Regression? | I would suggest using a generalised linear model (GLM) with a log-link function instead of directly log-transforming your variables; in R you can simply use glm with family= gaussian(link='log') to be | Lognormal Regression?
I would suggest using a generalised linear model (GLM) with a log-link function instead of directly log-transforming your variables; in R you can simply use glm with family= gaussian(link='log') to begin with.
I say this because modelling the mean of the log-transformed variable (as you would do ... | Lognormal Regression?
I would suggest using a generalised linear model (GLM) with a log-link function instead of directly log-transforming your variables; in R you can simply use glm with family= gaussian(link='log') to be |
39,241 | Lognormal Regression? | I want to take the log of the response variable and do a least-squares regression line over my predictive variable.
If I expected the relationship to be linear on the log scale, that's where I'd probably start.
Is this an okay thing to do?
It can be; it depends on what else is going on.
I know that for my original... | Lognormal Regression? | I want to take the log of the response variable and do a least-squares regression line over my predictive variable.
If I expected the relationship to be linear on the log scale, that's where I'd prob | Lognormal Regression?
I want to take the log of the response variable and do a least-squares regression line over my predictive variable.
If I expected the relationship to be linear on the log scale, that's where I'd probably start.
Is this an okay thing to do?
It can be; it depends on what else is going on.
I kno... | Lognormal Regression?
I want to take the log of the response variable and do a least-squares regression line over my predictive variable.
If I expected the relationship to be linear on the log scale, that's where I'd prob |
39,242 | How can you convert a gamma distribution into normal distribution? [closed] | Hope this answer does not seem facetious:
You can transform random variables from one to another with the inverse CDF method:
If $\gamma$ is Gamma distributed (with some fixed parameters), and $F$ its CDF then $F(\gamma)$ has uniform(0,1) distribution. Thus $\Phi^{-1}(F(\gamma))$ has Normal distribution.
This requires... | How can you convert a gamma distribution into normal distribution? [closed] | Hope this answer does not seem facetious:
You can transform random variables from one to another with the inverse CDF method:
If $\gamma$ is Gamma distributed (with some fixed parameters), and $F$ its | How can you convert a gamma distribution into normal distribution? [closed]
Hope this answer does not seem facetious:
You can transform random variables from one to another with the inverse CDF method:
If $\gamma$ is Gamma distributed (with some fixed parameters), and $F$ its CDF then $F(\gamma)$ has uniform(0,1) distr... | How can you convert a gamma distribution into normal distribution? [closed]
Hope this answer does not seem facetious:
You can transform random variables from one to another with the inverse CDF method:
If $\gamma$ is Gamma distributed (with some fixed parameters), and $F$ its |
39,243 | What is the terminology for data aggregated via summed totals versus data aggregated via means? | Properties that are physically additive are called extensive. Mass is extensive, as when you add (literally!) weights to a balance.
A feature of extensive properties is that totals make sense. In your example, gas used, measured in kWh, is one instance.
The word physically is not meant restrictively here. My income i... | What is the terminology for data aggregated via summed totals versus data aggregated via means? | Properties that are physically additive are called extensive. Mass is extensive, as when you add (literally!) weights to a balance.
A feature of extensive properties is that totals make sense. In you | What is the terminology for data aggregated via summed totals versus data aggregated via means?
Properties that are physically additive are called extensive. Mass is extensive, as when you add (literally!) weights to a balance.
A feature of extensive properties is that totals make sense. In your example, gas used, mea... | What is the terminology for data aggregated via summed totals versus data aggregated via means?
Properties that are physically additive are called extensive. Mass is extensive, as when you add (literally!) weights to a balance.
A feature of extensive properties is that totals make sense. In you |
39,244 | What is the terminology for data aggregated via summed totals versus data aggregated via means? | In database applications/data warehouse/BI it is common to refer to additive measures
additive example: money
semi-additive: balance (can aggregate across eg departments but not time)
non-additive: ratios (eg growth rate etc)
https://stackoverflow.com/questions/34295293/whats-the-difference-between-additive-semi-addit... | What is the terminology for data aggregated via summed totals versus data aggregated via means? | In database applications/data warehouse/BI it is common to refer to additive measures
additive example: money
semi-additive: balance (can aggregate across eg departments but not time)
non-additive: r | What is the terminology for data aggregated via summed totals versus data aggregated via means?
In database applications/data warehouse/BI it is common to refer to additive measures
additive example: money
semi-additive: balance (can aggregate across eg departments but not time)
non-additive: ratios (eg growth rate et... | What is the terminology for data aggregated via summed totals versus data aggregated via means?
In database applications/data warehouse/BI it is common to refer to additive measures
additive example: money
semi-additive: balance (can aggregate across eg departments but not time)
non-additive: r |
39,245 | Student's t-test with a covariate? | One common means of controlling for some other covariate would be via regression. Put the X and Y values into the response (DV), and a Y-group indicator (0 if in X, 1 if in Y) as a DV, along with your covariate (or some suitable proxy for it if the variable can't be measured directly) as another DV.
You regress on your... | Student's t-test with a covariate? | One common means of controlling for some other covariate would be via regression. Put the X and Y values into the response (DV), and a Y-group indicator (0 if in X, 1 if in Y) as a DV, along with your | Student's t-test with a covariate?
One common means of controlling for some other covariate would be via regression. Put the X and Y values into the response (DV), and a Y-group indicator (0 if in X, 1 if in Y) as a DV, along with your covariate (or some suitable proxy for it if the variable can't be measured directly)... | Student's t-test with a covariate?
One common means of controlling for some other covariate would be via regression. Put the X and Y values into the response (DV), and a Y-group indicator (0 if in X, 1 if in Y) as a DV, along with your |
39,246 | Student's t-test with a covariate? | By means of t-test you are assessing whether there is a significant difference between two sets of data --- e.g. the realizations of two random variables $X$ and $Y$. When using t-test you are doing a hypothesis test, and you can't control for any variable. To be more specific, when doing hypothesis tests you are not e... | Student's t-test with a covariate? | By means of t-test you are assessing whether there is a significant difference between two sets of data --- e.g. the realizations of two random variables $X$ and $Y$. When using t-test you are doing a | Student's t-test with a covariate?
By means of t-test you are assessing whether there is a significant difference between two sets of data --- e.g. the realizations of two random variables $X$ and $Y$. When using t-test you are doing a hypothesis test, and you can't control for any variable. To be more specific, when d... | Student's t-test with a covariate?
By means of t-test you are assessing whether there is a significant difference between two sets of data --- e.g. the realizations of two random variables $X$ and $Y$. When using t-test you are doing a |
39,247 | Why the sum of true positive and false positive does not have to be equal to one? | You have four conditional probabilities.
$$\text{tp} = \Pr(\text{detected}\mid\text{aircraft present})$$
$$\text{fn} =\Pr(\text{undetected}\mid\text{aircraft present})$$
$$\text{fp} = \Pr(\text{detected}\mid\text{aircraft not present})$$
$$\text{tn} = \Pr(\text{undetected}\mid\text{aircraft not present})$$
Using the de... | Why the sum of true positive and false positive does not have to be equal to one? | You have four conditional probabilities.
$$\text{tp} = \Pr(\text{detected}\mid\text{aircraft present})$$
$$\text{fn} =\Pr(\text{undetected}\mid\text{aircraft present})$$
$$\text{fp} = \Pr(\text{detect | Why the sum of true positive and false positive does not have to be equal to one?
You have four conditional probabilities.
$$\text{tp} = \Pr(\text{detected}\mid\text{aircraft present})$$
$$\text{fn} =\Pr(\text{undetected}\mid\text{aircraft present})$$
$$\text{fp} = \Pr(\text{detected}\mid\text{aircraft not present})$$
... | Why the sum of true positive and false positive does not have to be equal to one?
You have four conditional probabilities.
$$\text{tp} = \Pr(\text{detected}\mid\text{aircraft present})$$
$$\text{fn} =\Pr(\text{undetected}\mid\text{aircraft present})$$
$$\text{fp} = \Pr(\text{detect |
39,248 | Unable to estimate standard error after freeing first indicator in SEM model - Why is it so? | As Maarten points out, your problem is that you have not set the scale of the second model. True, you have more observed variances/covariances than what you need to identify your model, but you still need to provide a point of reference from which other model parameters can be calculated (Brown, 2015).
You can set the... | Unable to estimate standard error after freeing first indicator in SEM model - Why is it so? | As Maarten points out, your problem is that you have not set the scale of the second model. True, you have more observed variances/covariances than what you need to identify your model, but you still | Unable to estimate standard error after freeing first indicator in SEM model - Why is it so?
As Maarten points out, your problem is that you have not set the scale of the second model. True, you have more observed variances/covariances than what you need to identify your model, but you still need to provide a point of ... | Unable to estimate standard error after freeing first indicator in SEM model - Why is it so?
As Maarten points out, your problem is that you have not set the scale of the second model. True, you have more observed variances/covariances than what you need to identify your model, but you still |
39,249 | Unable to estimate standard error after freeing first indicator in SEM model - Why is it so? | Think of the scale of GnM. It is latent so it does not have a natural scale like meters (inches), euros (yen), etc. Instead we need to give it a scale by telling it when it is 0 and what a unit of increase is. In your original model you set GnM to 0 when all the indicators are 0, and by setting the loading of x1 to 1, ... | Unable to estimate standard error after freeing first indicator in SEM model - Why is it so? | Think of the scale of GnM. It is latent so it does not have a natural scale like meters (inches), euros (yen), etc. Instead we need to give it a scale by telling it when it is 0 and what a unit of inc | Unable to estimate standard error after freeing first indicator in SEM model - Why is it so?
Think of the scale of GnM. It is latent so it does not have a natural scale like meters (inches), euros (yen), etc. Instead we need to give it a scale by telling it when it is 0 and what a unit of increase is. In your original ... | Unable to estimate standard error after freeing first indicator in SEM model - Why is it so?
Think of the scale of GnM. It is latent so it does not have a natural scale like meters (inches), euros (yen), etc. Instead we need to give it a scale by telling it when it is 0 and what a unit of inc |
39,250 | Reliability in IRT Style | In Classical Test Theory observed test scores $X$ could be defined as:
$$X = T + E$$
where $T$ are the true scores and $E$ is an error of measurement. This means that their variance is:
$$\sigma^2_X = \sigma^2_T + \sigma^2_E$$
In this case, reliability could be defined as:
$$ \rho_{xx'} = \frac{\sigma^2_T}{\sigma^2_X} ... | Reliability in IRT Style | In Classical Test Theory observed test scores $X$ could be defined as:
$$X = T + E$$
where $T$ are the true scores and $E$ is an error of measurement. This means that their variance is:
$$\sigma^2_X = | Reliability in IRT Style
In Classical Test Theory observed test scores $X$ could be defined as:
$$X = T + E$$
where $T$ are the true scores and $E$ is an error of measurement. This means that their variance is:
$$\sigma^2_X = \sigma^2_T + \sigma^2_E$$
In this case, reliability could be defined as:
$$ \rho_{xx'} = \frac... | Reliability in IRT Style
In Classical Test Theory observed test scores $X$ could be defined as:
$$X = T + E$$
where $T$ are the true scores and $E$ is an error of measurement. This means that their variance is:
$$\sigma^2_X = |
39,251 | Reliability in IRT Style | To start off, let's look at what we mean by reliability. Reliability is often thought of as how consistent a measure will be across different measurement scenarios, with everything being equal except the occasion (same assessment, same conditions, same people, different day, e.g.). Reliability can also be thought of as... | Reliability in IRT Style | To start off, let's look at what we mean by reliability. Reliability is often thought of as how consistent a measure will be across different measurement scenarios, with everything being equal except | Reliability in IRT Style
To start off, let's look at what we mean by reliability. Reliability is often thought of as how consistent a measure will be across different measurement scenarios, with everything being equal except the occasion (same assessment, same conditions, same people, different day, e.g.). Reliability ... | Reliability in IRT Style
To start off, let's look at what we mean by reliability. Reliability is often thought of as how consistent a measure will be across different measurement scenarios, with everything being equal except |
39,252 | Difference in chi-squared calculated by anova from cph and coxph | Test differences
There are two differences between the two tests used:
The use of likelihood ratio tests versus Wald tests
The use of a sequential tests versus tests for the effect of one variable given the other variables
Since your example data set is huge (1822 complete observations, with 897 events) the first dif... | Difference in chi-squared calculated by anova from cph and coxph | Test differences
There are two differences between the two tests used:
The use of likelihood ratio tests versus Wald tests
The use of a sequential tests versus tests for the effect of one variable gi | Difference in chi-squared calculated by anova from cph and coxph
Test differences
There are two differences between the two tests used:
The use of likelihood ratio tests versus Wald tests
The use of a sequential tests versus tests for the effect of one variable given the other variables
Since your example data set is... | Difference in chi-squared calculated by anova from cph and coxph
Test differences
There are two differences between the two tests used:
The use of likelihood ratio tests versus Wald tests
The use of a sequential tests versus tests for the effect of one variable gi |
39,253 | What's the relation between deep learning and extreme learning machine? | Extreme learning machines and deep learning are slightly related, but advocate quite adversary concepts.
ELMs are neural nets with a single hidden layer, where the first weight matrix is initialized randomly. This allows the output matrix to be estimated via least squares, which is very quickly done.
Deep learning, on ... | What's the relation between deep learning and extreme learning machine? | Extreme learning machines and deep learning are slightly related, but advocate quite adversary concepts.
ELMs are neural nets with a single hidden layer, where the first weight matrix is initialized r | What's the relation between deep learning and extreme learning machine?
Extreme learning machines and deep learning are slightly related, but advocate quite adversary concepts.
ELMs are neural nets with a single hidden layer, where the first weight matrix is initialized randomly. This allows the output matrix to be est... | What's the relation between deep learning and extreme learning machine?
Extreme learning machines and deep learning are slightly related, but advocate quite adversary concepts.
ELMs are neural nets with a single hidden layer, where the first weight matrix is initialized r |
39,254 | What's the relation between deep learning and extreme learning machine? | The difference is: deep learning is original, while ELM is just a fancy name for 3 old methods.
The “extreme learning machines (ELM)” are indeed worth working on, but they just shouldn’t be called “ELM”. With annotated PDF files at http://elmorigin.wix.com/originofelm , you can easily verify the following facts within... | What's the relation between deep learning and extreme learning machine? | The difference is: deep learning is original, while ELM is just a fancy name for 3 old methods.
The “extreme learning machines (ELM)” are indeed worth working on, but they just shouldn’t be called “E | What's the relation between deep learning and extreme learning machine?
The difference is: deep learning is original, while ELM is just a fancy name for 3 old methods.
The “extreme learning machines (ELM)” are indeed worth working on, but they just shouldn’t be called “ELM”. With annotated PDF files at http://elmorigi... | What's the relation between deep learning and extreme learning machine?
The difference is: deep learning is original, while ELM is just a fancy name for 3 old methods.
The “extreme learning machines (ELM)” are indeed worth working on, but they just shouldn’t be called “E |
39,255 | Do random forest variable importance measures take into account the interactions? | The variable importance obtained by permutations is computed only by permuting values for a single variable. Thus, it computes some importance measure of the given variable in the context that all other data is fixed. I think it is reasonable to state that the importance measure includes in the measurement also interac... | Do random forest variable importance measures take into account the interactions? | The variable importance obtained by permutations is computed only by permuting values for a single variable. Thus, it computes some importance measure of the given variable in the context that all oth | Do random forest variable importance measures take into account the interactions?
The variable importance obtained by permutations is computed only by permuting values for a single variable. Thus, it computes some importance measure of the given variable in the context that all other data is fixed. I think it is reason... | Do random forest variable importance measures take into account the interactions?
The variable importance obtained by permutations is computed only by permuting values for a single variable. Thus, it computes some importance measure of the given variable in the context that all oth |
39,256 | Do random forest variable importance measures take into account the interactions? | Run this code and assert that RF variable importance do incorporate interactions.
library(randomForest)
obs=1000
vars =4
X = data.frame(replicate(vars,rnorm(obs)))
ysignal = with(X,sign(X1*X2))
ynoise = 0.1 * rnorm(obs)
y = ysignal + ynoise
RF = randomForest(X,y,importance=T)
varImpPlot(RF)
You should see X1 and X2 a... | Do random forest variable importance measures take into account the interactions? | Run this code and assert that RF variable importance do incorporate interactions.
library(randomForest)
obs=1000
vars =4
X = data.frame(replicate(vars,rnorm(obs)))
ysignal = with(X,sign(X1*X2))
ynoise | Do random forest variable importance measures take into account the interactions?
Run this code and assert that RF variable importance do incorporate interactions.
library(randomForest)
obs=1000
vars =4
X = data.frame(replicate(vars,rnorm(obs)))
ysignal = with(X,sign(X1*X2))
ynoise = 0.1 * rnorm(obs)
y = ysignal + yno... | Do random forest variable importance measures take into account the interactions?
Run this code and assert that RF variable importance do incorporate interactions.
library(randomForest)
obs=1000
vars =4
X = data.frame(replicate(vars,rnorm(obs)))
ysignal = with(X,sign(X1*X2))
ynoise |
39,257 | strucchange package on ARIMA model | The package strucchange requires as input the formula of a linear model to be passed to lm. I don't think there is a straightforward way to use the package with function arima. I don't know either any other R packages implementing this but I can give some basic guidelines that may be helpful for your purposes.
You can... | strucchange package on ARIMA model | The package strucchange requires as input the formula of a linear model to be passed to lm. I don't think there is a straightforward way to use the package with function arima. I don't know either any | strucchange package on ARIMA model
The package strucchange requires as input the formula of a linear model to be passed to lm. I don't think there is a straightforward way to use the package with function arima. I don't know either any other R packages implementing this but I can give some basic guidelines that may be ... | strucchange package on ARIMA model
The package strucchange requires as input the formula of a linear model to be passed to lm. I don't think there is a straightforward way to use the package with function arima. I don't know either any |
39,258 | strucchange package on ARIMA model | I have blogged about detecting structural break using the strucchange package in R. It is pretty straight forward - here's the outline:
# assuming you have a 'ts' object in R
# 1. install package 'strucchange'
# 2. Then write down this code:
library(strucchange)
# store the breakdates
bp_ts <- breakpoints(ts)
# th... | strucchange package on ARIMA model | I have blogged about detecting structural break using the strucchange package in R. It is pretty straight forward - here's the outline:
# assuming you have a 'ts' object in R
# 1. install package 's | strucchange package on ARIMA model
I have blogged about detecting structural break using the strucchange package in R. It is pretty straight forward - here's the outline:
# assuming you have a 'ts' object in R
# 1. install package 'strucchange'
# 2. Then write down this code:
library(strucchange)
# store the breakd... | strucchange package on ARIMA model
I have blogged about detecting structural break using the strucchange package in R. It is pretty straight forward - here's the outline:
# assuming you have a 'ts' object in R
# 1. install package 's |
39,259 | strucchange package on ARIMA model | If you want to check the existence of structure breaks, I would recommend you to:
Make outliers analysis, with the package tsoutliers. By using the function tso, you can check if your model has an isolated spike (additive outlier), an abrupt change in the mean level (level shift), a spike that takes a few periods to d... | strucchange package on ARIMA model | If you want to check the existence of structure breaks, I would recommend you to:
Make outliers analysis, with the package tsoutliers. By using the function tso, you can check if your model has an is | strucchange package on ARIMA model
If you want to check the existence of structure breaks, I would recommend you to:
Make outliers analysis, with the package tsoutliers. By using the function tso, you can check if your model has an isolated spike (additive outlier), an abrupt change in the mean level (level shift), a ... | strucchange package on ARIMA model
If you want to check the existence of structure breaks, I would recommend you to:
Make outliers analysis, with the package tsoutliers. By using the function tso, you can check if your model has an is |
39,260 | Countable intersection of almost sure events is also almost sure | Let's consider complements $B_n^c$ to $B_n$. For any $n$ it holds that $\mathbb{P}(B_n^c) = 0$.
Using countable additivity for measures we get:
$$
\mathbb{P} \left(\bigcap_{n = 1}^{\infty} B_n \right) = 1 - \mathbb{P}\left(\bigcup_{n = 1}^{\infty} B_n^c \right) \geq 1 - \sum_{n = 1}^{\infty} \mathbb{P}(B_n^c) = 1.
$$ ... | Countable intersection of almost sure events is also almost sure | Let's consider complements $B_n^c$ to $B_n$. For any $n$ it holds that $\mathbb{P}(B_n^c) = 0$.
Using countable additivity for measures we get:
$$
\mathbb{P} \left(\bigcap_{n = 1}^{\infty} B_n \right | Countable intersection of almost sure events is also almost sure
Let's consider complements $B_n^c$ to $B_n$. For any $n$ it holds that $\mathbb{P}(B_n^c) = 0$.
Using countable additivity for measures we get:
$$
\mathbb{P} \left(\bigcap_{n = 1}^{\infty} B_n \right) = 1 - \mathbb{P}\left(\bigcup_{n = 1}^{\infty} B_n^c ... | Countable intersection of almost sure events is also almost sure
Let's consider complements $B_n^c$ to $B_n$. For any $n$ it holds that $\mathbb{P}(B_n^c) = 0$.
Using countable additivity for measures we get:
$$
\mathbb{P} \left(\bigcap_{n = 1}^{\infty} B_n \right |
39,261 | Confidence Interval for a Random Sample Selected from Gamma Distribution | Edit: Time to add details, I think. The OP has long since worked it out but hasn't taken the invitation to write up a more complete solution, so I shall, in the interest of having a full answer to the question.
A pivot is a function of the data and the statistic whose distribution doesn't depend on the value of the sta... | Confidence Interval for a Random Sample Selected from Gamma Distribution | Edit: Time to add details, I think. The OP has long since worked it out but hasn't taken the invitation to write up a more complete solution, so I shall, in the interest of having a full answer to the | Confidence Interval for a Random Sample Selected from Gamma Distribution
Edit: Time to add details, I think. The OP has long since worked it out but hasn't taken the invitation to write up a more complete solution, so I shall, in the interest of having a full answer to the question.
A pivot is a function of the data an... | Confidence Interval for a Random Sample Selected from Gamma Distribution
Edit: Time to add details, I think. The OP has long since worked it out but hasn't taken the invitation to write up a more complete solution, so I shall, in the interest of having a full answer to the |
39,262 | How to do Simple Confirmatory Factory Analysis/SEM in R? | A CFA is pretty easy to do in R with OpenMx, sem, or lavaan. Since a CFA is such a vanilla case of SEM, all three are pretty easy to implement and offer helpful walkthroughs within their respective documentations. I personally use OpenMx or lavaan. One thing to keep in mind if you use OpenMx is that it won't give you f... | How to do Simple Confirmatory Factory Analysis/SEM in R? | A CFA is pretty easy to do in R with OpenMx, sem, or lavaan. Since a CFA is such a vanilla case of SEM, all three are pretty easy to implement and offer helpful walkthroughs within their respective do | How to do Simple Confirmatory Factory Analysis/SEM in R?
A CFA is pretty easy to do in R with OpenMx, sem, or lavaan. Since a CFA is such a vanilla case of SEM, all three are pretty easy to implement and offer helpful walkthroughs within their respective documentations. I personally use OpenMx or lavaan. One thing to k... | How to do Simple Confirmatory Factory Analysis/SEM in R?
A CFA is pretty easy to do in R with OpenMx, sem, or lavaan. Since a CFA is such a vanilla case of SEM, all three are pretty easy to implement and offer helpful walkthroughs within their respective do |
39,263 | Deliberately fitting a model without intercept [duplicate] | The intercept in a linear regression model may represent two totally different things:
A) Your theoretical model may lead you to a specification with a constant term. A basic example from Economics is when one wants to estimate the parameters of a production function (a statistical relationship that links output pro... | Deliberately fitting a model without intercept [duplicate] | The intercept in a linear regression model may represent two totally different things:
A) Your theoretical model may lead you to a specification with a constant term. A basic example from Economics | Deliberately fitting a model without intercept [duplicate]
The intercept in a linear regression model may represent two totally different things:
A) Your theoretical model may lead you to a specification with a constant term. A basic example from Economics is when one wants to estimate the parameters of a production... | Deliberately fitting a model without intercept [duplicate]
The intercept in a linear regression model may represent two totally different things:
A) Your theoretical model may lead you to a specification with a constant term. A basic example from Economics |
39,264 | Deliberately fitting a model without intercept [duplicate] | As several others have already mentioned, there are many situations where theory dictates a regression through the origin (e.g. y = corn production, x = amount of cultivated land; when x=0, y must be 0). In these situations, I would first run a traditional regression with a constant and slope term. Then, test whether t... | Deliberately fitting a model without intercept [duplicate] | As several others have already mentioned, there are many situations where theory dictates a regression through the origin (e.g. y = corn production, x = amount of cultivated land; when x=0, y must be | Deliberately fitting a model without intercept [duplicate]
As several others have already mentioned, there are many situations where theory dictates a regression through the origin (e.g. y = corn production, x = amount of cultivated land; when x=0, y must be 0). In these situations, I would first run a traditional regr... | Deliberately fitting a model without intercept [duplicate]
As several others have already mentioned, there are many situations where theory dictates a regression through the origin (e.g. y = corn production, x = amount of cultivated land; when x=0, y must be |
39,265 | How to regress a positive response variable that is also not a count variable? | On this information, many distributions could make sense: gamma, lognormal, etc., etc. In general, generalized linear models with various links (logarithm first and foremost) and various families could all apply.
By the way, the usefulness of Poisson is not limited to count variables; this is a common myth. See for ex... | How to regress a positive response variable that is also not a count variable? | On this information, many distributions could make sense: gamma, lognormal, etc., etc. In general, generalized linear models with various links (logarithm first and foremost) and various families coul | How to regress a positive response variable that is also not a count variable?
On this information, many distributions could make sense: gamma, lognormal, etc., etc. In general, generalized linear models with various links (logarithm first and foremost) and various families could all apply.
By the way, the usefulness ... | How to regress a positive response variable that is also not a count variable?
On this information, many distributions could make sense: gamma, lognormal, etc., etc. In general, generalized linear models with various links (logarithm first and foremost) and various families coul |
39,266 | What is an intuitive explanation of why we want homoskedasticity in a regression? | Homoskedasticity means that the variances of all the observations are identical to one another, heteroskedasticity means they're different. It's possible that the size of the variances displays some trend relative to x, but it's not strictly necessary; as shown in the accompanying diagram, variances that are different... | What is an intuitive explanation of why we want homoskedasticity in a regression? | Homoskedasticity means that the variances of all the observations are identical to one another, heteroskedasticity means they're different. It's possible that the size of the variances displays some | What is an intuitive explanation of why we want homoskedasticity in a regression?
Homoskedasticity means that the variances of all the observations are identical to one another, heteroskedasticity means they're different. It's possible that the size of the variances displays some trend relative to x, but it's not stri... | What is an intuitive explanation of why we want homoskedasticity in a regression?
Homoskedasticity means that the variances of all the observations are identical to one another, heteroskedasticity means they're different. It's possible that the size of the variances displays some |
39,267 | What is an intuitive explanation of why we want homoskedasticity in a regression? | Why do we want homoskedasticity in regression?
It's not that we want homoskedasticity or heteroskedasticity in the regression; what we want is for the model to reflect the actual properties of the data. Regression models may be formulated either with an assumption of homoskedasticity, or with an assumption of heteros... | What is an intuitive explanation of why we want homoskedasticity in a regression? | Why do we want homoskedasticity in regression?
It's not that we want homoskedasticity or heteroskedasticity in the regression; what we want is for the model to reflect the actual properties of the da | What is an intuitive explanation of why we want homoskedasticity in a regression?
Why do we want homoskedasticity in regression?
It's not that we want homoskedasticity or heteroskedasticity in the regression; what we want is for the model to reflect the actual properties of the data. Regression models may be formulat... | What is an intuitive explanation of why we want homoskedasticity in a regression?
Why do we want homoskedasticity in regression?
It's not that we want homoskedasticity or heteroskedasticity in the regression; what we want is for the model to reflect the actual properties of the da |
39,268 | What is an intuitive explanation of why we want homoskedasticity in a regression? | In addition to the other excellent answers:
Can someone explain intuitively why this is necessary? (An applied example would be great!)
Constant variance isn't necessary, but when it holds modeling and analysis is simpler. Part of this must be historical, analysis when variance is not constant is more complicated, re... | What is an intuitive explanation of why we want homoskedasticity in a regression? | In addition to the other excellent answers:
Can someone explain intuitively why this is necessary? (An applied example would be great!)
Constant variance isn't necessary, but when it holds modeling | What is an intuitive explanation of why we want homoskedasticity in a regression?
In addition to the other excellent answers:
Can someone explain intuitively why this is necessary? (An applied example would be great!)
Constant variance isn't necessary, but when it holds modeling and analysis is simpler. Part of this ... | What is an intuitive explanation of why we want homoskedasticity in a regression?
In addition to the other excellent answers:
Can someone explain intuitively why this is necessary? (An applied example would be great!)
Constant variance isn't necessary, but when it holds modeling |
39,269 | What is an intuitive explanation of why we want homoskedasticity in a regression? | One of the assumptions of OLS regression is:
Variance of the error term/residual is constant. This assumption is known as homoskedasticity.
This assumption ensures that with the change in observations, the variations in the error term should not change
If this condition is violated, the ordinary least square estimat... | What is an intuitive explanation of why we want homoskedasticity in a regression? | One of the assumptions of OLS regression is:
Variance of the error term/residual is constant. This assumption is known as homoskedasticity.
This assumption ensures that with the change in observatio | What is an intuitive explanation of why we want homoskedasticity in a regression?
One of the assumptions of OLS regression is:
Variance of the error term/residual is constant. This assumption is known as homoskedasticity.
This assumption ensures that with the change in observations, the variations in the error term s... | What is an intuitive explanation of why we want homoskedasticity in a regression?
One of the assumptions of OLS regression is:
Variance of the error term/residual is constant. This assumption is known as homoskedasticity.
This assumption ensures that with the change in observatio |
39,270 | In statistics what does NA stand for? | In datasets, NA can mean:
"Not Available": e.g. the sensor was down at the time of the measure,
"Not Applicable": e.g. when asking a bachelor the name of his wife,
"No Answer": e.g. the respondent to a questionnaire skipped a question. | In statistics what does NA stand for? | In datasets, NA can mean:
"Not Available": e.g. the sensor was down at the time of the measure,
"Not Applicable": e.g. when asking a bachelor the name of his wife,
"No Answer": e.g. the respondent to | In statistics what does NA stand for?
In datasets, NA can mean:
"Not Available": e.g. the sensor was down at the time of the measure,
"Not Applicable": e.g. when asking a bachelor the name of his wife,
"No Answer": e.g. the respondent to a questionnaire skipped a question. | In statistics what does NA stand for?
In datasets, NA can mean:
"Not Available": e.g. the sensor was down at the time of the measure,
"Not Applicable": e.g. when asking a bachelor the name of his wife,
"No Answer": e.g. the respondent to |
39,271 | Instrumental variable Tobit in R | You ask for two things in the question. First you ask for a way to consistently estimate this model in R. Second, you ask for the particular ways implemented in Stata's ivtobit (these ways are full-information maximum likelihood and the Newey, 1987 two-step estimator). Doing the second would be a nice service for th... | Instrumental variable Tobit in R | You ask for two things in the question. First you ask for a way to consistently estimate this model in R. Second, you ask for the particular ways implemented in Stata's ivtobit (these ways are full- | Instrumental variable Tobit in R
You ask for two things in the question. First you ask for a way to consistently estimate this model in R. Second, you ask for the particular ways implemented in Stata's ivtobit (these ways are full-information maximum likelihood and the Newey, 1987 two-step estimator). Doing the seco... | Instrumental variable Tobit in R
You ask for two things in the question. First you ask for a way to consistently estimate this model in R. Second, you ask for the particular ways implemented in Stata's ivtobit (these ways are full- |
39,272 | Open source libraries in science [duplicate] | I don't consider this an R specific question. The real question is: can you trust other people's code? Or, taking the other perspective: do you think you can do better? (in the time you are willing/able to spend)
Whether the software is open or closed source, does not really matter. The trustworthiness of open source c... | Open source libraries in science [duplicate] | I don't consider this an R specific question. The real question is: can you trust other people's code? Or, taking the other perspective: do you think you can do better? (in the time you are willing/ab | Open source libraries in science [duplicate]
I don't consider this an R specific question. The real question is: can you trust other people's code? Or, taking the other perspective: do you think you can do better? (in the time you are willing/able to spend)
Whether the software is open or closed source, does not really... | Open source libraries in science [duplicate]
I don't consider this an R specific question. The real question is: can you trust other people's code? Or, taking the other perspective: do you think you can do better? (in the time you are willing/ab |
39,273 | Is there a situation under which the distribution of p-values is skewed towards 1? | It is also possible to have the effect in practice, when the null hypothesis is true but not all the assumption for the test are given. For example, the classical (non Welch) t-Test assumes equal variance in both groups. In the case that both groups are equally sized a violation is usually not that bad, otherwise the n... | Is there a situation under which the distribution of p-values is skewed towards 1? | It is also possible to have the effect in practice, when the null hypothesis is true but not all the assumption for the test are given. For example, the classical (non Welch) t-Test assumes equal vari | Is there a situation under which the distribution of p-values is skewed towards 1?
It is also possible to have the effect in practice, when the null hypothesis is true but not all the assumption for the test are given. For example, the classical (non Welch) t-Test assumes equal variance in both groups. In the case that... | Is there a situation under which the distribution of p-values is skewed towards 1?
It is also possible to have the effect in practice, when the null hypothesis is true but not all the assumption for the test are given. For example, the classical (non Welch) t-Test assumes equal vari |
39,274 | Is there a situation under which the distribution of p-values is skewed towards 1? | That can happen in a one-sided test when your "true" parameter is inside the region of the null hypothesis but not on the boundary. Consider the following example in Stata where the "true" parameter (in this case mean) is 1:
clear all
program define sim, rclass
drop _all
set obs 100
gen x = rnormal(1,1)
... | Is there a situation under which the distribution of p-values is skewed towards 1? | That can happen in a one-sided test when your "true" parameter is inside the region of the null hypothesis but not on the boundary. Consider the following example in Stata where the "true" parameter ( | Is there a situation under which the distribution of p-values is skewed towards 1?
That can happen in a one-sided test when your "true" parameter is inside the region of the null hypothesis but not on the boundary. Consider the following example in Stata where the "true" parameter (in this case mean) is 1:
clear all
p... | Is there a situation under which the distribution of p-values is skewed towards 1?
That can happen in a one-sided test when your "true" parameter is inside the region of the null hypothesis but not on the boundary. Consider the following example in Stata where the "true" parameter ( |
39,275 | Free econometrics textbooks | Hyndman and Athanasopoulos. Forecasting: principles and practice, OTexts
is an introductory textbook on forecasting covering prediction using regression models and forecasting with univariate time series models such as ETS and ARIMA. | Free econometrics textbooks | Hyndman and Athanasopoulos. Forecasting: principles and practice, OTexts
is an introductory textbook on forecasting covering prediction using regression models and forecasting with univariate time ser | Free econometrics textbooks
Hyndman and Athanasopoulos. Forecasting: principles and practice, OTexts
is an introductory textbook on forecasting covering prediction using regression models and forecasting with univariate time series models such as ETS and ARIMA. | Free econometrics textbooks
Hyndman and Athanasopoulos. Forecasting: principles and practice, OTexts
is an introductory textbook on forecasting covering prediction using regression models and forecasting with univariate time ser |
39,276 | Free econometrics textbooks | Graeme, I think Hansen is the best reference in that category; it is clearly and rigorously written.
Imbens and Wooldridge produced a nice NBER summer course, see here (with video). It assumes some knowledge of basic econometrics, which you probably have already. They more or less repeated it at the UK Centre for Micro... | Free econometrics textbooks | Graeme, I think Hansen is the best reference in that category; it is clearly and rigorously written.
Imbens and Wooldridge produced a nice NBER summer course, see here (with video). It assumes some kn | Free econometrics textbooks
Graeme, I think Hansen is the best reference in that category; it is clearly and rigorously written.
Imbens and Wooldridge produced a nice NBER summer course, see here (with video). It assumes some knowledge of basic econometrics, which you probably have already. They more or less repeated i... | Free econometrics textbooks
Graeme, I think Hansen is the best reference in that category; it is clearly and rigorously written.
Imbens and Wooldridge produced a nice NBER summer course, see here (with video). It assumes some kn |
39,277 | Free econometrics textbooks | Econometrics by Michael Creel is a project to develop a document for teaching graduate econometrics that is "open source", specifically, licensed as GNU GPL.
About the book:
This document integrates lecture notes for a one year graduate level
course with computer programs that illustrate and apply the methods
that... | Free econometrics textbooks | Econometrics by Michael Creel is a project to develop a document for teaching graduate econometrics that is "open source", specifically, licensed as GNU GPL.
About the book:
This document integrates | Free econometrics textbooks
Econometrics by Michael Creel is a project to develop a document for teaching graduate econometrics that is "open source", specifically, licensed as GNU GPL.
About the book:
This document integrates lecture notes for a one year graduate level
course with computer programs that illustrate ... | Free econometrics textbooks
Econometrics by Michael Creel is a project to develop a document for teaching graduate econometrics that is "open source", specifically, licensed as GNU GPL.
About the book:
This document integrates |
39,278 | Free econometrics textbooks | It is worth throwing into the mix that Greene (5th edition) is free online for self-study. Wonderful resource:
https://spu.fem.uniag.sk/cvicenia/ksov/obtulovic/Mana%C5%BE.%20%C5%A1tatistika%20a%20ekonometria/EconometricsGREENE.pdf | Free econometrics textbooks | It is worth throwing into the mix that Greene (5th edition) is free online for self-study. Wonderful resource:
https://spu.fem.uniag.sk/cvicenia/ksov/obtulovic/Mana%C5%BE.%20%C5%A1tatistika%20a%20eko | Free econometrics textbooks
It is worth throwing into the mix that Greene (5th edition) is free online for self-study. Wonderful resource:
https://spu.fem.uniag.sk/cvicenia/ksov/obtulovic/Mana%C5%BE.%20%C5%A1tatistika%20a%20ekonometria/EconometricsGREENE.pdf | Free econometrics textbooks
It is worth throwing into the mix that Greene (5th edition) is free online for self-study. Wonderful resource:
https://spu.fem.uniag.sk/cvicenia/ksov/obtulovic/Mana%C5%BE.%20%C5%A1tatistika%20a%20eko |
39,279 | Differences and relation between retrospective power analysis and a posteriori power analysis? | Assume for simplicity that your model is defined by only one parameter $\theta$. The power is the function $\theta \mapsto \Pr(\text{reject } H_0 \mid \theta)$, which depends on the sample size $n$.
In Retrospective Power Analysis, you only plug in your estimate $\theta$: you look at the value $\Pr(\text{reject } H_0 \... | Differences and relation between retrospective power analysis and a posteriori power analysis? | Assume for simplicity that your model is defined by only one parameter $\theta$. The power is the function $\theta \mapsto \Pr(\text{reject } H_0 \mid \theta)$, which depends on the sample size $n$.
I | Differences and relation between retrospective power analysis and a posteriori power analysis?
Assume for simplicity that your model is defined by only one parameter $\theta$. The power is the function $\theta \mapsto \Pr(\text{reject } H_0 \mid \theta)$, which depends on the sample size $n$.
In Retrospective Power Ana... | Differences and relation between retrospective power analysis and a posteriori power analysis?
Assume for simplicity that your model is defined by only one parameter $\theta$. The power is the function $\theta \mapsto \Pr(\text{reject } H_0 \mid \theta)$, which depends on the sample size $n$.
I |
39,280 | Differences and relation between retrospective power analysis and a posteriori power analysis? | When you do an a posteriori power analysis you have to plug in an effect size and a variance. If you plug in the observed effect size and variance, as you do in an a posteriori power analysis, you are assuming that the true effect size is the same as the observed. | Differences and relation between retrospective power analysis and a posteriori power analysis? | When you do an a posteriori power analysis you have to plug in an effect size and a variance. If you plug in the observed effect size and variance, as you do in an a posteriori power analysis, you are | Differences and relation between retrospective power analysis and a posteriori power analysis?
When you do an a posteriori power analysis you have to plug in an effect size and a variance. If you plug in the observed effect size and variance, as you do in an a posteriori power analysis, you are assuming that the true e... | Differences and relation between retrospective power analysis and a posteriori power analysis?
When you do an a posteriori power analysis you have to plug in an effect size and a variance. If you plug in the observed effect size and variance, as you do in an a posteriori power analysis, you are |
39,281 | Mean of log of cdf | No,you'll have to do the integration numerically for each $c$ value.
Let $\Phi(x)$ be the Gaussian CDF function.
You want to evaluate
$$
I=\int \Phi'(x-c) \ln \Phi(x) dx
$$
Idea 1: this looks like a convolution; the Fourier transform of $\Phi'$ is easy enough, but that of $\ln \Phi$ is not; and even if you could take... | Mean of log of cdf | No,you'll have to do the integration numerically for each $c$ value.
Let $\Phi(x)$ be the Gaussian CDF function.
You want to evaluate
$$
I=\int \Phi'(x-c) \ln \Phi(x) dx
$$
Idea 1: this looks like a | Mean of log of cdf
No,you'll have to do the integration numerically for each $c$ value.
Let $\Phi(x)$ be the Gaussian CDF function.
You want to evaluate
$$
I=\int \Phi'(x-c) \ln \Phi(x) dx
$$
Idea 1: this looks like a convolution; the Fourier transform of $\Phi'$ is easy enough, but that of $\ln \Phi$ is not; and eve... | Mean of log of cdf
No,you'll have to do the integration numerically for each $c$ value.
Let $\Phi(x)$ be the Gaussian CDF function.
You want to evaluate
$$
I=\int \Phi'(x-c) \ln \Phi(x) dx
$$
Idea 1: this looks like a |
39,282 | Mean of log of cdf | In short there is no "easy" way to do this, and Dave has tried a few sensible approaches. One approach perhaps worth a try is to taylor expand since your function is smooth and analytic, and we have a simple form for the moments of the standard normal:
Notice that
$L^* = \ln(\Phi(X))$
$L = \frac{\phi(x)}{\Phi(x)}$
$L'... | Mean of log of cdf | In short there is no "easy" way to do this, and Dave has tried a few sensible approaches. One approach perhaps worth a try is to taylor expand since your function is smooth and analytic, and we have | Mean of log of cdf
In short there is no "easy" way to do this, and Dave has tried a few sensible approaches. One approach perhaps worth a try is to taylor expand since your function is smooth and analytic, and we have a simple form for the moments of the standard normal:
Notice that
$L^* = \ln(\Phi(X))$
$L = \frac{\ph... | Mean of log of cdf
In short there is no "easy" way to do this, and Dave has tried a few sensible approaches. One approach perhaps worth a try is to taylor expand since your function is smooth and analytic, and we have |
39,283 | Fuzzy regression discontinuity design and exclusion restriction | I am not a fan of the Angrist and Pischke book, but they do have a flair for phrasing, and as they say, fuzzy RD is IV (Sec. 6.2). This fact is obscured by the fact that the instrument is essentially a nonlinear transformation (step function) of one of the included exogenous variables, which by virtue of the conditiona... | Fuzzy regression discontinuity design and exclusion restriction | I am not a fan of the Angrist and Pischke book, but they do have a flair for phrasing, and as they say, fuzzy RD is IV (Sec. 6.2). This fact is obscured by the fact that the instrument is essentially | Fuzzy regression discontinuity design and exclusion restriction
I am not a fan of the Angrist and Pischke book, but they do have a flair for phrasing, and as they say, fuzzy RD is IV (Sec. 6.2). This fact is obscured by the fact that the instrument is essentially a nonlinear transformation (step function) of one of the... | Fuzzy regression discontinuity design and exclusion restriction
I am not a fan of the Angrist and Pischke book, but they do have a flair for phrasing, and as they say, fuzzy RD is IV (Sec. 6.2). This fact is obscured by the fact that the instrument is essentially |
39,284 | Fuzzy regression discontinuity design and exclusion restriction | This is only a partial answer to the question I think you're asking.
I think with RD we assume that conditional on treatment, the other variables are smooth functions of the assignment variable $z$. This means that the outcome variable $y$ should jump at the cutoff only because of the discontinuity in the level of trea... | Fuzzy regression discontinuity design and exclusion restriction | This is only a partial answer to the question I think you're asking.
I think with RD we assume that conditional on treatment, the other variables are smooth functions of the assignment variable $z$. T | Fuzzy regression discontinuity design and exclusion restriction
This is only a partial answer to the question I think you're asking.
I think with RD we assume that conditional on treatment, the other variables are smooth functions of the assignment variable $z$. This means that the outcome variable $y$ should jump at t... | Fuzzy regression discontinuity design and exclusion restriction
This is only a partial answer to the question I think you're asking.
I think with RD we assume that conditional on treatment, the other variables are smooth functions of the assignment variable $z$. T |
39,285 | Fuzzy regression discontinuity design and exclusion restriction | There is no need for an explicit exclusion restriction in the fuzzy regression discontinuity design. The assumption that the potential outcomes are continuous in the neighbourhood of the cutoff (or globally) together with the discontinuity of the probability to receive treatment at the cutoff implicitly acts as a (loca... | Fuzzy regression discontinuity design and exclusion restriction | There is no need for an explicit exclusion restriction in the fuzzy regression discontinuity design. The assumption that the potential outcomes are continuous in the neighbourhood of the cutoff (or gl | Fuzzy regression discontinuity design and exclusion restriction
There is no need for an explicit exclusion restriction in the fuzzy regression discontinuity design. The assumption that the potential outcomes are continuous in the neighbourhood of the cutoff (or globally) together with the discontinuity of the probabili... | Fuzzy regression discontinuity design and exclusion restriction
There is no need for an explicit exclusion restriction in the fuzzy regression discontinuity design. The assumption that the potential outcomes are continuous in the neighbourhood of the cutoff (or gl |
39,286 | Why is random assignment important in stratified sampling? | You have not correctly interpreted user697473's claim. He is not talking about failing to include any data from brand C. He was talking about giving a particular vector of assignemnts $0$ probability. He was not saying that you can magically determine the value of some variable while never testing it. He wants to be ab... | Why is random assignment important in stratified sampling? | You have not correctly interpreted user697473's claim. He is not talking about failing to include any data from brand C. He was talking about giving a particular vector of assignemnts $0$ probability. | Why is random assignment important in stratified sampling?
You have not correctly interpreted user697473's claim. He is not talking about failing to include any data from brand C. He was talking about giving a particular vector of assignemnts $0$ probability. He was not saying that you can magically determine the value... | Why is random assignment important in stratified sampling?
You have not correctly interpreted user697473's claim. He is not talking about failing to include any data from brand C. He was talking about giving a particular vector of assignemnts $0$ probability. |
39,287 | Why is random assignment important in stratified sampling? | Michael, I did not say that when you exclude a group from randomization you can still get unbiased estimates, and I would never argue in favor of this idea; this is certainly not true. What is true is that you don't have to have the same sampling rates, and that's the imbalance that you can correct with weights. You il... | Why is random assignment important in stratified sampling? | Michael, I did not say that when you exclude a group from randomization you can still get unbiased estimates, and I would never argue in favor of this idea; this is certainly not true. What is true is | Why is random assignment important in stratified sampling?
Michael, I did not say that when you exclude a group from randomization you can still get unbiased estimates, and I would never argue in favor of this idea; this is certainly not true. What is true is that you don't have to have the same sampling rates, and tha... | Why is random assignment important in stratified sampling?
Michael, I did not say that when you exclude a group from randomization you can still get unbiased estimates, and I would never argue in favor of this idea; this is certainly not true. What is true is |
39,288 | How to interpret model diagnostics when doing linear regression in R? | This is a long and rambling question, so you are getting a long and rambling answer. Apologies. Using the example from the ?lm() call,
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4.69)
group <- gl(2,10,20, labels=c("Ctl","Trt"))
weight <- c(ctl, trt)... | How to interpret model diagnostics when doing linear regression in R? | This is a long and rambling question, so you are getting a long and rambling answer. Apologies. Using the example from the ?lm() call,
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt < | How to interpret model diagnostics when doing linear regression in R?
This is a long and rambling question, so you are getting a long and rambling answer. Apologies. Using the example from the ?lm() call,
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt <- c(4.81,4.17,4.41,3.59,5.87,3.83,6.03,4.89,4.32,4... | How to interpret model diagnostics when doing linear regression in R?
This is a long and rambling question, so you are getting a long and rambling answer. Apologies. Using the example from the ?lm() call,
ctl <- c(4.17,5.58,5.18,6.11,4.50,4.61,5.17,4.53,5.33,5.14)
trt < |
39,289 | Bayesion priors in ridge regression with scikit learn's linear model | Ridge regression looks like:
$$
\min_{\beta}||Y-X\beta||^2 + \lambda_1 ||\beta||^2
$$
If you want to instead compute
$$
\beta^* = \arg\min_{\beta}||Y-X\beta||^2 + \lambda_1 ||\beta - \beta_0||^2
$$
I guess you could just turn this into shrinking towards zero using the new variable
$$\theta = \beta - \beta_0.$$
So you'd... | Bayesion priors in ridge regression with scikit learn's linear model | Ridge regression looks like:
$$
\min_{\beta}||Y-X\beta||^2 + \lambda_1 ||\beta||^2
$$
If you want to instead compute
$$
\beta^* = \arg\min_{\beta}||Y-X\beta||^2 + \lambda_1 ||\beta - \beta_0||^2
$$
I | Bayesion priors in ridge regression with scikit learn's linear model
Ridge regression looks like:
$$
\min_{\beta}||Y-X\beta||^2 + \lambda_1 ||\beta||^2
$$
If you want to instead compute
$$
\beta^* = \arg\min_{\beta}||Y-X\beta||^2 + \lambda_1 ||\beta - \beta_0||^2
$$
I guess you could just turn this into shrinking towar... | Bayesion priors in ridge regression with scikit learn's linear model
Ridge regression looks like:
$$
\min_{\beta}||Y-X\beta||^2 + \lambda_1 ||\beta||^2
$$
If you want to instead compute
$$
\beta^* = \arg\min_{\beta}||Y-X\beta||^2 + \lambda_1 ||\beta - \beta_0||^2
$$
I |
39,290 | Bayesion priors in ridge regression with scikit learn's linear model | What's posted in the only answer by Dapz does not do what it's supposed to do.
If I choose a value > 0 for any of the $\beta_0$, say the "i-th", the corresponding $\beta^*$ of "i" will be lower than with standard ridge regression, instead of higher as it should be (because we penalize for moving away from something > 0... | Bayesion priors in ridge regression with scikit learn's linear model | What's posted in the only answer by Dapz does not do what it's supposed to do.
If I choose a value > 0 for any of the $\beta_0$, say the "i-th", the corresponding $\beta^*$ of "i" will be lower than w | Bayesion priors in ridge regression with scikit learn's linear model
What's posted in the only answer by Dapz does not do what it's supposed to do.
If I choose a value > 0 for any of the $\beta_0$, say the "i-th", the corresponding $\beta^*$ of "i" will be lower than with standard ridge regression, instead of higher as... | Bayesion priors in ridge regression with scikit learn's linear model
What's posted in the only answer by Dapz does not do what it's supposed to do.
If I choose a value > 0 for any of the $\beta_0$, say the "i-th", the corresponding $\beta^*$ of "i" will be lower than w |
39,291 | Bayesion priors in ridge regression with scikit learn's linear model | I think this code should work, implementing the solution suggested by others above:
def fit_with_prior(model, X, y, sample_weight=None, prior=None):
"""Fit a regularized model with a nonzero prior"""
assert prior is not None, "you need to specify a prior"
new_y = y - np.sum(prior * X, axis=1)
model.fit(... | Bayesion priors in ridge regression with scikit learn's linear model | I think this code should work, implementing the solution suggested by others above:
def fit_with_prior(model, X, y, sample_weight=None, prior=None):
"""Fit a regularized model with a nonzero prior | Bayesion priors in ridge regression with scikit learn's linear model
I think this code should work, implementing the solution suggested by others above:
def fit_with_prior(model, X, y, sample_weight=None, prior=None):
"""Fit a regularized model with a nonzero prior"""
assert prior is not None, "you need to spec... | Bayesion priors in ridge regression with scikit learn's linear model
I think this code should work, implementing the solution suggested by others above:
def fit_with_prior(model, X, y, sample_weight=None, prior=None):
"""Fit a regularized model with a nonzero prior |
39,292 | How to correlate two time series, with possible time differences | Apply a lag operator on one time series, with the other fixed, and calculate the coherence of the cross-spectrum achieved against each lag. Find the lag that gives you the maximum coherence and interpret it.
Coherence is computed at each frequency-and hence is a vector. Hence, a sum of a weighted coherence would be a g... | How to correlate two time series, with possible time differences | Apply a lag operator on one time series, with the other fixed, and calculate the coherence of the cross-spectrum achieved against each lag. Find the lag that gives you the maximum coherence and interp | How to correlate two time series, with possible time differences
Apply a lag operator on one time series, with the other fixed, and calculate the coherence of the cross-spectrum achieved against each lag. Find the lag that gives you the maximum coherence and interpret it.
Coherence is computed at each frequency-and hen... | How to correlate two time series, with possible time differences
Apply a lag operator on one time series, with the other fixed, and calculate the coherence of the cross-spectrum achieved against each lag. Find the lag that gives you the maximum coherence and interp |
39,293 | How to correlate two time series, with possible time differences | The cross correlation function will give you the Pearson correlation for 2 time-series at different time lags. The R function is ccf().
For further study, a Granger causality test tries to determine a cause-effect relationship between the 2 correlated series by first removing the serial correlation in TS1 (the stock pr... | How to correlate two time series, with possible time differences | The cross correlation function will give you the Pearson correlation for 2 time-series at different time lags. The R function is ccf().
For further study, a Granger causality test tries to determine a | How to correlate two time series, with possible time differences
The cross correlation function will give you the Pearson correlation for 2 time-series at different time lags. The R function is ccf().
For further study, a Granger causality test tries to determine a cause-effect relationship between the 2 correlated ser... | How to correlate two time series, with possible time differences
The cross correlation function will give you the Pearson correlation for 2 time-series at different time lags. The R function is ccf().
For further study, a Granger causality test tries to determine a |
39,294 | Estimating kappa of von Mises distribution | According to Banerjee et al., Clustering on the Unit Hypersphere using
von Mises-Fisher Distributions (J. Mach. Learning Res. 6 (2005)), you can estimate the von Mises-Fisher parameters $\mu$ and $\kappa$ with maximum likelihood.
Let $x_i$ be the $n$ points in dimension $d$ from your sample.
Let $r = \sum_i x_i$.
Let $... | Estimating kappa of von Mises distribution | According to Banerjee et al., Clustering on the Unit Hypersphere using
von Mises-Fisher Distributions (J. Mach. Learning Res. 6 (2005)), you can estimate the von Mises-Fisher parameters $\mu$ and $\ka | Estimating kappa of von Mises distribution
According to Banerjee et al., Clustering on the Unit Hypersphere using
von Mises-Fisher Distributions (J. Mach. Learning Res. 6 (2005)), you can estimate the von Mises-Fisher parameters $\mu$ and $\kappa$ with maximum likelihood.
Let $x_i$ be the $n$ points in dimension $d$ fr... | Estimating kappa of von Mises distribution
According to Banerjee et al., Clustering on the Unit Hypersphere using
von Mises-Fisher Distributions (J. Mach. Learning Res. 6 (2005)), you can estimate the von Mises-Fisher parameters $\mu$ and $\ka |
39,295 | Estimating kappa of von Mises distribution | Check out the est.kappa() function in the CircStats package for R:
Computes the maximum likelihood estimate of kappa, the concentration parameter of a von Mises distribution, given a set of angular measurements. | Estimating kappa of von Mises distribution | Check out the est.kappa() function in the CircStats package for R:
Computes the maximum likelihood estimate of kappa, the concentration parameter of a von Mises distribution, given a set of angular m | Estimating kappa of von Mises distribution
Check out the est.kappa() function in the CircStats package for R:
Computes the maximum likelihood estimate of kappa, the concentration parameter of a von Mises distribution, given a set of angular measurements. | Estimating kappa of von Mises distribution
Check out the est.kappa() function in the CircStats package for R:
Computes the maximum likelihood estimate of kappa, the concentration parameter of a von Mises distribution, given a set of angular m |
39,296 | Estimating kappa of von Mises distribution | Yes, the Von-Mises distribution family is an exponential family, so you can find the maximum likelihood estimate of its parameters as you would for any exponential family: set the expectation parameters to the average of the sufficient statistics $T(x) = x$ whose magnitude we'll call $\bar r$. You'll have to convert t... | Estimating kappa of von Mises distribution | Yes, the Von-Mises distribution family is an exponential family, so you can find the maximum likelihood estimate of its parameters as you would for any exponential family: set the expectation paramete | Estimating kappa of von Mises distribution
Yes, the Von-Mises distribution family is an exponential family, so you can find the maximum likelihood estimate of its parameters as you would for any exponential family: set the expectation parameters to the average of the sufficient statistics $T(x) = x$ whose magnitude we'... | Estimating kappa of von Mises distribution
Yes, the Von-Mises distribution family is an exponential family, so you can find the maximum likelihood estimate of its parameters as you would for any exponential family: set the expectation paramete |
39,297 | Plot of the estimated log hazard ratio in R | Saw this question while browsing through the survival questions and though I just post some of my personal favorites. I like using the rms package for survival functions and you could do a forestplot type of output:
library(survival)
library(rms)
ddist <- datadist(ovarian)
options(datadist='ddist')
ovarian$rx <- facto... | Plot of the estimated log hazard ratio in R | Saw this question while browsing through the survival questions and though I just post some of my personal favorites. I like using the rms package for survival functions and you could do a forestplot | Plot of the estimated log hazard ratio in R
Saw this question while browsing through the survival questions and though I just post some of my personal favorites. I like using the rms package for survival functions and you could do a forestplot type of output:
library(survival)
library(rms)
ddist <- datadist(ovarian)
op... | Plot of the estimated log hazard ratio in R
Saw this question while browsing through the survival questions and though I just post some of my personal favorites. I like using the rms package for survival functions and you could do a forestplot |
39,298 | Plot of the estimated log hazard ratio in R | Not having a copy of Applied Survival Analysis, I'm guessing you're looking for something like this: http://rgm2.lab.nig.ac.jp/RGM2/func.php?rd_id=Design:hazard.ratio.plot | Plot of the estimated log hazard ratio in R | Not having a copy of Applied Survival Analysis, I'm guessing you're looking for something like this: http://rgm2.lab.nig.ac.jp/RGM2/func.php?rd_id=Design:hazard.ratio.plot | Plot of the estimated log hazard ratio in R
Not having a copy of Applied Survival Analysis, I'm guessing you're looking for something like this: http://rgm2.lab.nig.ac.jp/RGM2/func.php?rd_id=Design:hazard.ratio.plot | Plot of the estimated log hazard ratio in R
Not having a copy of Applied Survival Analysis, I'm guessing you're looking for something like this: http://rgm2.lab.nig.ac.jp/RGM2/func.php?rd_id=Design:hazard.ratio.plot |
39,299 | What "more" does differencing (d>0) do in ARIMA than detrend? | Differencing isn't actually the preferred way of removing a trend---detrending is. Detrending involves estimating the trend and calculating the deviation from the estimated trend in any particular period.
The main use of differencing is to remove the problem of unit roots. A unit root arises, for example, when $\rho=1$... | What "more" does differencing (d>0) do in ARIMA than detrend? | Differencing isn't actually the preferred way of removing a trend---detrending is. Detrending involves estimating the trend and calculating the deviation from the estimated trend in any particular per | What "more" does differencing (d>0) do in ARIMA than detrend?
Differencing isn't actually the preferred way of removing a trend---detrending is. Detrending involves estimating the trend and calculating the deviation from the estimated trend in any particular period.
The main use of differencing is to remove the problem... | What "more" does differencing (d>0) do in ARIMA than detrend?
Differencing isn't actually the preferred way of removing a trend---detrending is. Detrending involves estimating the trend and calculating the deviation from the estimated trend in any particular per |
39,300 | What "more" does differencing (d>0) do in ARIMA than detrend? | Unnecessary differencing or filtering can inject structure (see Slutsky Effect: http://mathworld.wolfram.com/Slutzky-YuleEffect.html, https://www.minneapolisfed.org/publications/the-region/the-meaning-of-slutsky, https://blog.minitab.com/blog/understanding-statistics/the-ghost-pattern-a-haunting-cautionary-tale-ab... | What "more" does differencing (d>0) do in ARIMA than detrend? | Unnecessary differencing or filtering can inject structure (see Slutsky Effect: http://mathworld.wolfram.com/Slutzky-YuleEffect.html, https://www.minneapolisfed.org/publications/the-region/the-mean | What "more" does differencing (d>0) do in ARIMA than detrend?
Unnecessary differencing or filtering can inject structure (see Slutsky Effect: http://mathworld.wolfram.com/Slutzky-YuleEffect.html, https://www.minneapolisfed.org/publications/the-region/the-meaning-of-slutsky, https://blog.minitab.com/blog/understand... | What "more" does differencing (d>0) do in ARIMA than detrend?
Unnecessary differencing or filtering can inject structure (see Slutsky Effect: http://mathworld.wolfram.com/Slutzky-YuleEffect.html, https://www.minneapolisfed.org/publications/the-region/the-mean |
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