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 |
|---|---|---|---|---|---|---|
15,201 | A 6th response option ("I don't know") was added to a 5-point Likert scale. Is the data lost? | If this was a standardized questionnaire that has been validated independently, then you cannot claim that the new questionnaire is equivalent, and the data is no longer comparable. You could try to validate and examine the questionnaire in separate experiments (very time- and effort-consuming, especially if you also w... | A 6th response option ("I don't know") was added to a 5-point Likert scale. Is the data lost? | If this was a standardized questionnaire that has been validated independently, then you cannot claim that the new questionnaire is equivalent, and the data is no longer comparable. You could try to v | A 6th response option ("I don't know") was added to a 5-point Likert scale. Is the data lost?
If this was a standardized questionnaire that has been validated independently, then you cannot claim that the new questionnaire is equivalent, and the data is no longer comparable. You could try to validate and examine the qu... | A 6th response option ("I don't know") was added to a 5-point Likert scale. Is the data lost?
If this was a standardized questionnaire that has been validated independently, then you cannot claim that the new questionnaire is equivalent, and the data is no longer comparable. You could try to v |
15,202 | A 6th response option ("I don't know") was added to a 5-point Likert scale. Is the data lost? | If you ask how many children has the respondent given birth to, the answers "zero" and "not applicable" would not mean strictly the same thing, since men cannot give birth.
For some contexts, equating "I don't know" to the neutral response could be, likewise, a conceptual mistake.
Actually, you have two questions: a di... | A 6th response option ("I don't know") was added to a 5-point Likert scale. Is the data lost? | If you ask how many children has the respondent given birth to, the answers "zero" and "not applicable" would not mean strictly the same thing, since men cannot give birth.
For some contexts, equating | A 6th response option ("I don't know") was added to a 5-point Likert scale. Is the data lost?
If you ask how many children has the respondent given birth to, the answers "zero" and "not applicable" would not mean strictly the same thing, since men cannot give birth.
For some contexts, equating "I don't know" to the neu... | A 6th response option ("I don't know") was added to a 5-point Likert scale. Is the data lost?
If you ask how many children has the respondent given birth to, the answers "zero" and "not applicable" would not mean strictly the same thing, since men cannot give birth.
For some contexts, equating |
15,203 | A 6th response option ("I don't know") was added to a 5-point Likert scale. Is the data lost? | The dilemma whether one should include or not the Don't know response option into a questionnaire consisting of rating scales of Likert type is eternal. Often, when the items ask about opinion, the DK is included because having no opinion is an important status on its own and the option as such is expected by responden... | A 6th response option ("I don't know") was added to a 5-point Likert scale. Is the data lost? | The dilemma whether one should include or not the Don't know response option into a questionnaire consisting of rating scales of Likert type is eternal. Often, when the items ask about opinion, the DK | A 6th response option ("I don't know") was added to a 5-point Likert scale. Is the data lost?
The dilemma whether one should include or not the Don't know response option into a questionnaire consisting of rating scales of Likert type is eternal. Often, when the items ask about opinion, the DK is included because havin... | A 6th response option ("I don't know") was added to a 5-point Likert scale. Is the data lost?
The dilemma whether one should include or not the Don't know response option into a questionnaire consisting of rating scales of Likert type is eternal. Often, when the items ask about opinion, the DK |
15,204 | A 6th response option ("I don't know") was added to a 5-point Likert scale. Is the data lost? | You now have respondents self-selected for having an opinion on the matter. Whatever you conclude will solely be about those people. This might be OK, as polling those "don't knows" is by definition less useful. | A 6th response option ("I don't know") was added to a 5-point Likert scale. Is the data lost? | You now have respondents self-selected for having an opinion on the matter. Whatever you conclude will solely be about those people. This might be OK, as polling those "don't knows" is by definition l | A 6th response option ("I don't know") was added to a 5-point Likert scale. Is the data lost?
You now have respondents self-selected for having an opinion on the matter. Whatever you conclude will solely be about those people. This might be OK, as polling those "don't knows" is by definition less useful. | A 6th response option ("I don't know") was added to a 5-point Likert scale. Is the data lost?
You now have respondents self-selected for having an opinion on the matter. Whatever you conclude will solely be about those people. This might be OK, as polling those "don't knows" is by definition l |
15,205 | Are there any non-distance based clustering algorithms? | One example of such a method are Finite Mixture Models (e.g. here or here) used for clustering. In FMM you consider the distribution ($f$) of your variable $X$ as a mixture of $K$ distributions ($f_1,...,f_k$):
$$f(x, \vartheta) = \sum^K_{k=1} \pi_k f_k(x, \vartheta_k)$$
where $\vartheta$ is a vector of parameters $\va... | Are there any non-distance based clustering algorithms? | One example of such a method are Finite Mixture Models (e.g. here or here) used for clustering. In FMM you consider the distribution ($f$) of your variable $X$ as a mixture of $K$ distributions ($f_1, | Are there any non-distance based clustering algorithms?
One example of such a method are Finite Mixture Models (e.g. here or here) used for clustering. In FMM you consider the distribution ($f$) of your variable $X$ as a mixture of $K$ distributions ($f_1,...,f_k$):
$$f(x, \vartheta) = \sum^K_{k=1} \pi_k f_k(x, \varthe... | Are there any non-distance based clustering algorithms?
One example of such a method are Finite Mixture Models (e.g. here or here) used for clustering. In FMM you consider the distribution ($f$) of your variable $X$ as a mixture of $K$ distributions ($f_1, |
15,206 | Are there any non-distance based clustering algorithms? | K-means isn't "really" distance based. It minimizes the variance. (But variance $\sim$ squared Euclidean distances; so every point is assigned to the nearest centroid by Euclidean distance, too).
There are plenty of grid-based clustering approaches. They don't compute distances because that would often yield quadratic ... | Are there any non-distance based clustering algorithms? | K-means isn't "really" distance based. It minimizes the variance. (But variance $\sim$ squared Euclidean distances; so every point is assigned to the nearest centroid by Euclidean distance, too).
Ther | Are there any non-distance based clustering algorithms?
K-means isn't "really" distance based. It minimizes the variance. (But variance $\sim$ squared Euclidean distances; so every point is assigned to the nearest centroid by Euclidean distance, too).
There are plenty of grid-based clustering approaches. They don't com... | Are there any non-distance based clustering algorithms?
K-means isn't "really" distance based. It minimizes the variance. (But variance $\sim$ squared Euclidean distances; so every point is assigned to the nearest centroid by Euclidean distance, too).
Ther |
15,207 | Are there any non-distance based clustering algorithms? | In addition to previous nice answers, I would suggest considering Dirichlet mixture models and Bayesian-based hierarchical Dirichlet process models. For a rather comprehensive and general overview of approaches and methods for determining an optimal number of clusters, please see this excellent answer on StackOverflow:... | Are there any non-distance based clustering algorithms? | In addition to previous nice answers, I would suggest considering Dirichlet mixture models and Bayesian-based hierarchical Dirichlet process models. For a rather comprehensive and general overview of | Are there any non-distance based clustering algorithms?
In addition to previous nice answers, I would suggest considering Dirichlet mixture models and Bayesian-based hierarchical Dirichlet process models. For a rather comprehensive and general overview of approaches and methods for determining an optimal number of clus... | Are there any non-distance based clustering algorithms?
In addition to previous nice answers, I would suggest considering Dirichlet mixture models and Bayesian-based hierarchical Dirichlet process models. For a rather comprehensive and general overview of |
15,208 | Are there any non-distance based clustering algorithms? | A purely discriminative approach is "regularized information maximisation" by Gomes et al. There is no notion of similarity/distance involved in it whatsoever.
The idea is to have a logistic regression like model that puts points into bins. But instead of training it to maximise some form of log-likelihood of the class... | Are there any non-distance based clustering algorithms? | A purely discriminative approach is "regularized information maximisation" by Gomes et al. There is no notion of similarity/distance involved in it whatsoever.
The idea is to have a logistic regressio | Are there any non-distance based clustering algorithms?
A purely discriminative approach is "regularized information maximisation" by Gomes et al. There is no notion of similarity/distance involved in it whatsoever.
The idea is to have a logistic regression like model that puts points into bins. But instead of training... | Are there any non-distance based clustering algorithms?
A purely discriminative approach is "regularized information maximisation" by Gomes et al. There is no notion of similarity/distance involved in it whatsoever.
The idea is to have a logistic regressio |
15,209 | Mathematical demonstration of the distance concentration in high dimensions | There is a simple mathematical thought experiment that sheds light on this phenomenon, although it might not seem immediately applicable. I will therefore describe this experiment briefly and follow that, in a separate section, by a computer analysis of a concrete situation.
A Thought Experiment
An old cartographic c... | Mathematical demonstration of the distance concentration in high dimensions | There is a simple mathematical thought experiment that sheds light on this phenomenon, although it might not seem immediately applicable. I will therefore describe this experiment briefly and follow | Mathematical demonstration of the distance concentration in high dimensions
There is a simple mathematical thought experiment that sheds light on this phenomenon, although it might not seem immediately applicable. I will therefore describe this experiment briefly and follow that, in a separate section, by a computer a... | Mathematical demonstration of the distance concentration in high dimensions
There is a simple mathematical thought experiment that sheds light on this phenomenon, although it might not seem immediately applicable. I will therefore describe this experiment briefly and follow |
15,210 | Mathematical demonstration of the distance concentration in high dimensions | Note that this depends on a) the distance measure (you are probably referring to the Euclidean distance) and b) the underlying measure/probability distribution, according to which you specify what "almost all" means. Surely all kinds of values and distributions for the distances are possible if you don't specify these.... | Mathematical demonstration of the distance concentration in high dimensions | Note that this depends on a) the distance measure (you are probably referring to the Euclidean distance) and b) the underlying measure/probability distribution, according to which you specify what "al | Mathematical demonstration of the distance concentration in high dimensions
Note that this depends on a) the distance measure (you are probably referring to the Euclidean distance) and b) the underlying measure/probability distribution, according to which you specify what "almost all" means. Surely all kinds of values ... | Mathematical demonstration of the distance concentration in high dimensions
Note that this depends on a) the distance measure (you are probably referring to the Euclidean distance) and b) the underlying measure/probability distribution, according to which you specify what "al |
15,211 | Can I use a tiny Validation set? | Larger validation sets give more accurate estimates of out-of-sample performance. But as you've noticed, at some point that estimate might be as accurate as you need it to be, and you can make some rough predictions as to the validation sample size you need to reach that point.
For simple correct/incorrect classificati... | Can I use a tiny Validation set? | Larger validation sets give more accurate estimates of out-of-sample performance. But as you've noticed, at some point that estimate might be as accurate as you need it to be, and you can make some ro | Can I use a tiny Validation set?
Larger validation sets give more accurate estimates of out-of-sample performance. But as you've noticed, at some point that estimate might be as accurate as you need it to be, and you can make some rough predictions as to the validation sample size you need to reach that point.
For simp... | Can I use a tiny Validation set?
Larger validation sets give more accurate estimates of out-of-sample performance. But as you've noticed, at some point that estimate might be as accurate as you need it to be, and you can make some ro |
15,212 | Can I use a tiny Validation set? | Nice discussion of this problem is provided by Andrew Ng on his Deep Learning course on Coursera.org. As he notes, the standard splits like 8:2, or 9:1 are valid if your data is small to moderately big, but many present day machine learning problems use huge amounts of data (e.g. millions of observations as in your cas... | Can I use a tiny Validation set? | Nice discussion of this problem is provided by Andrew Ng on his Deep Learning course on Coursera.org. As he notes, the standard splits like 8:2, or 9:1 are valid if your data is small to moderately bi | Can I use a tiny Validation set?
Nice discussion of this problem is provided by Andrew Ng on his Deep Learning course on Coursera.org. As he notes, the standard splits like 8:2, or 9:1 are valid if your data is small to moderately big, but many present day machine learning problems use huge amounts of data (e.g. millio... | Can I use a tiny Validation set?
Nice discussion of this problem is provided by Andrew Ng on his Deep Learning course on Coursera.org. As he notes, the standard splits like 8:2, or 9:1 are valid if your data is small to moderately bi |
15,213 | Can I use a tiny Validation set? | In the article Asymptotic Statistical Theory of Overtraining and Cross-Validation by Shun-ichi Amari et al. [1] they study the optimal amount of samples to leave out as a validation set (for the purpose of early stopping) and conclude that the optimal split is $1/\sqrt{2N}$, where $N$ is the number of samples available... | Can I use a tiny Validation set? | In the article Asymptotic Statistical Theory of Overtraining and Cross-Validation by Shun-ichi Amari et al. [1] they study the optimal amount of samples to leave out as a validation set (for the purpo | Can I use a tiny Validation set?
In the article Asymptotic Statistical Theory of Overtraining and Cross-Validation by Shun-ichi Amari et al. [1] they study the optimal amount of samples to leave out as a validation set (for the purpose of early stopping) and conclude that the optimal split is $1/\sqrt{2N}$, where $N$ i... | Can I use a tiny Validation set?
In the article Asymptotic Statistical Theory of Overtraining and Cross-Validation by Shun-ichi Amari et al. [1] they study the optimal amount of samples to leave out as a validation set (for the purpo |
15,214 | How to perform PCA for data of very high dimensionality? | The easiest way to do standard PCA is to center the columns of your data matrix (assuming the columns correspond to different variables) by subtracting the column means, and then perform an SVD. The left singular vectors, multiplied by the corresponding singular value, correspond to the (estimated) principal component... | How to perform PCA for data of very high dimensionality? | The easiest way to do standard PCA is to center the columns of your data matrix (assuming the columns correspond to different variables) by subtracting the column means, and then perform an SVD. The | How to perform PCA for data of very high dimensionality?
The easiest way to do standard PCA is to center the columns of your data matrix (assuming the columns correspond to different variables) by subtracting the column means, and then perform an SVD. The left singular vectors, multiplied by the corresponding singular... | How to perform PCA for data of very high dimensionality?
The easiest way to do standard PCA is to center the columns of your data matrix (assuming the columns correspond to different variables) by subtracting the column means, and then perform an SVD. The |
15,215 | How to perform PCA for data of very high dimensionality? | What you're doing right now is close, but you need to make sure you multiply the eigenvectors of (data . data.T) / lines on the left by data.T, in order to get the eigenvectors of (data.T . data) / lines. This is sometimes called the "transpose trick".
Here are some more details. Suppose you have a matrix $A$ that you ... | How to perform PCA for data of very high dimensionality? | What you're doing right now is close, but you need to make sure you multiply the eigenvectors of (data . data.T) / lines on the left by data.T, in order to get the eigenvectors of (data.T . data) / li | How to perform PCA for data of very high dimensionality?
What you're doing right now is close, but you need to make sure you multiply the eigenvectors of (data . data.T) / lines on the left by data.T, in order to get the eigenvectors of (data.T . data) / lines. This is sometimes called the "transpose trick".
Here are s... | How to perform PCA for data of very high dimensionality?
What you're doing right now is close, but you need to make sure you multiply the eigenvectors of (data . data.T) / lines on the left by data.T, in order to get the eigenvectors of (data.T . data) / li |
15,216 | How to perform PCA for data of very high dimensionality? | It sounds like what you want is the NIPALS algorithm for performing PCA. It's a very popular algorithm among statisticians. It has many advantages:
Computationally less expensive than SVD or eigenvalue decomposition methods if only the first few components are required.
Has more modest storage requirements in general ... | How to perform PCA for data of very high dimensionality? | It sounds like what you want is the NIPALS algorithm for performing PCA. It's a very popular algorithm among statisticians. It has many advantages:
Computationally less expensive than SVD or eigenval | How to perform PCA for data of very high dimensionality?
It sounds like what you want is the NIPALS algorithm for performing PCA. It's a very popular algorithm among statisticians. It has many advantages:
Computationally less expensive than SVD or eigenvalue decomposition methods if only the first few components are r... | How to perform PCA for data of very high dimensionality?
It sounds like what you want is the NIPALS algorithm for performing PCA. It's a very popular algorithm among statisticians. It has many advantages:
Computationally less expensive than SVD or eigenval |
15,217 | How to perform PCA for data of very high dimensionality? | To add on Gilead's answer, they are computationally less expensive algorithms for truncated PCAs. NIPALS is indeed very popular, but I have had a lot of success with approximate methods that perform a succession of fits on partial data (what is often called PCA by random projection). This was discussed in a metaoptimiz... | How to perform PCA for data of very high dimensionality? | To add on Gilead's answer, they are computationally less expensive algorithms for truncated PCAs. NIPALS is indeed very popular, but I have had a lot of success with approximate methods that perform a | How to perform PCA for data of very high dimensionality?
To add on Gilead's answer, they are computationally less expensive algorithms for truncated PCAs. NIPALS is indeed very popular, but I have had a lot of success with approximate methods that perform a succession of fits on partial data (what is often called PCA b... | How to perform PCA for data of very high dimensionality?
To add on Gilead's answer, they are computationally less expensive algorithms for truncated PCAs. NIPALS is indeed very popular, but I have had a lot of success with approximate methods that perform a |
15,218 | How to perform PCA for data of very high dimensionality? | If it helps someone, just adding on top of @raegtin's post, the following R code demonstrates how high-dimensional PCA can be done faster and more efficiently.
For the following matrix $A_{m\times n}$ with $n>>m$, the matrix $A^TA$ is a $n\times n$ matrix and not a full-rank matrix, having rank $=m$, so that only the ... | How to perform PCA for data of very high dimensionality? | If it helps someone, just adding on top of @raegtin's post, the following R code demonstrates how high-dimensional PCA can be done faster and more efficiently.
For the following matrix $A_{m\times n}$ | How to perform PCA for data of very high dimensionality?
If it helps someone, just adding on top of @raegtin's post, the following R code demonstrates how high-dimensional PCA can be done faster and more efficiently.
For the following matrix $A_{m\times n}$ with $n>>m$, the matrix $A^TA$ is a $n\times n$ matrix and no... | How to perform PCA for data of very high dimensionality?
If it helps someone, just adding on top of @raegtin's post, the following R code demonstrates how high-dimensional PCA can be done faster and more efficiently.
For the following matrix $A_{m\times n}$ |
15,219 | Speed in m/s is normally distributed, but same data expressed as "Time for 10 meters" is not | The image below illustrates intuitively why the transformed variable has a different distribution:
I have drawn two parallel lines.
On the lowest line I have plotted evenly spaced points at $0.1, 0.2, ..., 1.1, 1.2$ which represent the velocity $v$.
On the upper line I have draw points according to the formula $t=0.1/... | Speed in m/s is normally distributed, but same data expressed as "Time for 10 meters" is not | The image below illustrates intuitively why the transformed variable has a different distribution:
I have drawn two parallel lines.
On the lowest line I have plotted evenly spaced points at $0.1, 0.2 | Speed in m/s is normally distributed, but same data expressed as "Time for 10 meters" is not
The image below illustrates intuitively why the transformed variable has a different distribution:
I have drawn two parallel lines.
On the lowest line I have plotted evenly spaced points at $0.1, 0.2, ..., 1.1, 1.2$ which repr... | Speed in m/s is normally distributed, but same data expressed as "Time for 10 meters" is not
The image below illustrates intuitively why the transformed variable has a different distribution:
I have drawn two parallel lines.
On the lowest line I have plotted evenly spaced points at $0.1, 0.2 |
15,220 | Speed in m/s is normally distributed, but same data expressed as "Time for 10 meters" is not | Yes, this is an instance of inverse Gaussian. It has been observed that there is an inverse relationship between the cumulant generating function of the time to cover a unit distance and the cumulant generating function of the distance covered in a unit time. Because the distance covered in a unit time (in this case, w... | Speed in m/s is normally distributed, but same data expressed as "Time for 10 meters" is not | Yes, this is an instance of inverse Gaussian. It has been observed that there is an inverse relationship between the cumulant generating function of the time to cover a unit distance and the cumulant | Speed in m/s is normally distributed, but same data expressed as "Time for 10 meters" is not
Yes, this is an instance of inverse Gaussian. It has been observed that there is an inverse relationship between the cumulant generating function of the time to cover a unit distance and the cumulant generating function of the ... | Speed in m/s is normally distributed, but same data expressed as "Time for 10 meters" is not
Yes, this is an instance of inverse Gaussian. It has been observed that there is an inverse relationship between the cumulant generating function of the time to cover a unit distance and the cumulant |
15,221 | What regression/estimation is not a MLE? | Least squares is indeed maximum likelihood if the errors are iid normal, but if they aren't iid normal, least squares is not maximum likelihood. For example if my errors were logistic, least squares wouldn't be a terrible idea but it wouldn't be maximum likelihood.
Lots of estimators are not maximum likelihood estimato... | What regression/estimation is not a MLE? | Least squares is indeed maximum likelihood if the errors are iid normal, but if they aren't iid normal, least squares is not maximum likelihood. For example if my errors were logistic, least squares w | What regression/estimation is not a MLE?
Least squares is indeed maximum likelihood if the errors are iid normal, but if they aren't iid normal, least squares is not maximum likelihood. For example if my errors were logistic, least squares wouldn't be a terrible idea but it wouldn't be maximum likelihood.
Lots of estim... | What regression/estimation is not a MLE?
Least squares is indeed maximum likelihood if the errors are iid normal, but if they aren't iid normal, least squares is not maximum likelihood. For example if my errors were logistic, least squares w |
15,222 | What regression/estimation is not a MLE? | All MLE is minimax but not all minimax is MLE. Some examples of minimax estimators that do not maximize a likelihood are ROC regression, conditional logistic regression, Cox proportional hazards models, nearest neighbor, quasilikelihood, the list goes on and on. Hodge's "superefficient" estimator beats maximum likelih... | What regression/estimation is not a MLE? | All MLE is minimax but not all minimax is MLE. Some examples of minimax estimators that do not maximize a likelihood are ROC regression, conditional logistic regression, Cox proportional hazards model | What regression/estimation is not a MLE?
All MLE is minimax but not all minimax is MLE. Some examples of minimax estimators that do not maximize a likelihood are ROC regression, conditional logistic regression, Cox proportional hazards models, nearest neighbor, quasilikelihood, the list goes on and on. Hodge's "superef... | What regression/estimation is not a MLE?
All MLE is minimax but not all minimax is MLE. Some examples of minimax estimators that do not maximize a likelihood are ROC regression, conditional logistic regression, Cox proportional hazards model |
15,223 | What regression/estimation is not a MLE? | Bayesian approaches do not involve maximizing a likelihood function, but rather integrating over a posterior distribution. Note that the underlying model may be exactly identical (i.e., linear regression, generalized linear regression), but we also need to provide a prior distribution which captures our uncertainty in ... | What regression/estimation is not a MLE? | Bayesian approaches do not involve maximizing a likelihood function, but rather integrating over a posterior distribution. Note that the underlying model may be exactly identical (i.e., linear regress | What regression/estimation is not a MLE?
Bayesian approaches do not involve maximizing a likelihood function, but rather integrating over a posterior distribution. Note that the underlying model may be exactly identical (i.e., linear regression, generalized linear regression), but we also need to provide a prior distri... | What regression/estimation is not a MLE?
Bayesian approaches do not involve maximizing a likelihood function, but rather integrating over a posterior distribution. Note that the underlying model may be exactly identical (i.e., linear regress |
15,224 | What regression/estimation is not a MLE? | $$
Y_i = \alpha + \beta x_i + \varepsilon_i
$$
$\alpha,\beta$ are non-random and not observable.
$\varepsilon_i$ are random and not observable.
$x_i$ are non-random and are observable.
$Y_i$ are consequently random, and are observable.
Suppose you have the Gauss–Markov assumptions:
The errors $\varepsilon_i$ have ex... | What regression/estimation is not a MLE? | $$
Y_i = \alpha + \beta x_i + \varepsilon_i
$$
$\alpha,\beta$ are non-random and not observable.
$\varepsilon_i$ are random and not observable.
$x_i$ are non-random and are observable.
$Y_i$ are cons | What regression/estimation is not a MLE?
$$
Y_i = \alpha + \beta x_i + \varepsilon_i
$$
$\alpha,\beta$ are non-random and not observable.
$\varepsilon_i$ are random and not observable.
$x_i$ are non-random and are observable.
$Y_i$ are consequently random, and are observable.
Suppose you have the Gauss–Markov assumpt... | What regression/estimation is not a MLE?
$$
Y_i = \alpha + \beta x_i + \varepsilon_i
$$
$\alpha,\beta$ are non-random and not observable.
$\varepsilon_i$ are random and not observable.
$x_i$ are non-random and are observable.
$Y_i$ are cons |
15,225 | What regression/estimation is not a MLE? | An answer to the question "What regression/estimation is not a MLE?", a simple and robust alternative to Least-Squares (LS) is reportedly Least-Absolute Deviation (LAD).
To quote a source:
"The least absolute deviations method (LAD) is one of the principal alternatives to the least-squares method when one seeks to esti... | What regression/estimation is not a MLE? | An answer to the question "What regression/estimation is not a MLE?", a simple and robust alternative to Least-Squares (LS) is reportedly Least-Absolute Deviation (LAD).
To quote a source:
"The least | What regression/estimation is not a MLE?
An answer to the question "What regression/estimation is not a MLE?", a simple and robust alternative to Least-Squares (LS) is reportedly Least-Absolute Deviation (LAD).
To quote a source:
"The least absolute deviations method (LAD) is one of the principal alternatives to the le... | What regression/estimation is not a MLE?
An answer to the question "What regression/estimation is not a MLE?", a simple and robust alternative to Least-Squares (LS) is reportedly Least-Absolute Deviation (LAD).
To quote a source:
"The least |
15,226 | Family of GLM represents the distribution of the response variable or residuals? | The family argument for glm models determines the distribution family for the conditional distribution of the response, not of the residuals (except for the quasi-models).
Look at this way: For the usual linear regression, we can write the model as
$$Y_i \sim \text{Normal}(\beta_0+x_i^T\beta, \sigma^2).
$$
This means... | Family of GLM represents the distribution of the response variable or residuals? | The family argument for glm models determines the distribution family for the conditional distribution of the response, not of the residuals (except for the quasi-models).
Look at this way: For the u | Family of GLM represents the distribution of the response variable or residuals?
The family argument for glm models determines the distribution family for the conditional distribution of the response, not of the residuals (except for the quasi-models).
Look at this way: For the usual linear regression, we can write th... | Family of GLM represents the distribution of the response variable or residuals?
The family argument for glm models determines the distribution family for the conditional distribution of the response, not of the residuals (except for the quasi-models).
Look at this way: For the u |
15,227 | Family of GLM represents the distribution of the response variable or residuals? | Further to Kjetil's excellent answer, I wanted to add some specific examples to help clarify the meaning of a conditional distribution, which can be a bit of an elusive concept.
Let's say you took a random sample of 100 fish from a lake and you are interested in seeing how the age of the fish affects several outcome va... | Family of GLM represents the distribution of the response variable or residuals? | Further to Kjetil's excellent answer, I wanted to add some specific examples to help clarify the meaning of a conditional distribution, which can be a bit of an elusive concept.
Let's say you took a r | Family of GLM represents the distribution of the response variable or residuals?
Further to Kjetil's excellent answer, I wanted to add some specific examples to help clarify the meaning of a conditional distribution, which can be a bit of an elusive concept.
Let's say you took a random sample of 100 fish from a lake an... | Family of GLM represents the distribution of the response variable or residuals?
Further to Kjetil's excellent answer, I wanted to add some specific examples to help clarify the meaning of a conditional distribution, which can be a bit of an elusive concept.
Let's say you took a r |
15,228 | Adaptive GAM smooths in mgcv | Most of the extra smooths in the mgcv toolbox are really there for specialist applications — you can largely ignore them for general GAMs, especially univariate smooths (you don't need a random effect spline, a spline on the sphere, a Markov random field, or a soap-film smoother if you have univariate data for example.... | Adaptive GAM smooths in mgcv | Most of the extra smooths in the mgcv toolbox are really there for specialist applications — you can largely ignore them for general GAMs, especially univariate smooths (you don't need a random effect | Adaptive GAM smooths in mgcv
Most of the extra smooths in the mgcv toolbox are really there for specialist applications — you can largely ignore them for general GAMs, especially univariate smooths (you don't need a random effect spline, a spline on the sphere, a Markov random field, or a soap-film smoother if you have... | Adaptive GAM smooths in mgcv
Most of the extra smooths in the mgcv toolbox are really there for specialist applications — you can largely ignore them for general GAMs, especially univariate smooths (you don't need a random effect |
15,229 | A gallery of charts, diagrams, and plot types | Nathan Yau's books might be useful for people starting off, but are pitched lower than most visitors here should want to reach. I gave a very qualified 4 stars to "Data points" on amazon.com as can be seen at http://www.amazon.com/Data-Points-Visualization-Means-Something/dp/111846219X/
I can be very much more positiv... | A gallery of charts, diagrams, and plot types | Nathan Yau's books might be useful for people starting off, but are pitched lower than most visitors here should want to reach. I gave a very qualified 4 stars to "Data points" on amazon.com as can be | A gallery of charts, diagrams, and plot types
Nathan Yau's books might be useful for people starting off, but are pitched lower than most visitors here should want to reach. I gave a very qualified 4 stars to "Data points" on amazon.com as can be seen at http://www.amazon.com/Data-Points-Visualization-Means-Something/d... | A gallery of charts, diagrams, and plot types
Nathan Yau's books might be useful for people starting off, but are pitched lower than most visitors here should want to reach. I gave a very qualified 4 stars to "Data points" on amazon.com as can be |
15,230 | A gallery of charts, diagrams, and plot types | I personally prefer the D3 gallery because many of the plots there are dynamic and interactive (not to mention incredibly appealing and professional-looking from a graphic design perspective). There's also a tremendous range of variability in the style of plots and the type of information being displayed, so it makes ... | A gallery of charts, diagrams, and plot types | I personally prefer the D3 gallery because many of the plots there are dynamic and interactive (not to mention incredibly appealing and professional-looking from a graphic design perspective). There' | A gallery of charts, diagrams, and plot types
I personally prefer the D3 gallery because many of the plots there are dynamic and interactive (not to mention incredibly appealing and professional-looking from a graphic design perspective). There's also a tremendous range of variability in the style of plots and the typ... | A gallery of charts, diagrams, and plot types
I personally prefer the D3 gallery because many of the plots there are dynamic and interactive (not to mention incredibly appealing and professional-looking from a graphic design perspective). There' |
15,231 | A gallery of charts, diagrams, and plot types | There is the R Graphical Manual. Although it is presumably somewhat less useful for people who use other software, you can still look up topics and see some examples of possibilities that you could then try to reproduce by other means. | A gallery of charts, diagrams, and plot types | There is the R Graphical Manual. Although it is presumably somewhat less useful for people who use other software, you can still look up topics and see some examples of possibilities that you could t | A gallery of charts, diagrams, and plot types
There is the R Graphical Manual. Although it is presumably somewhat less useful for people who use other software, you can still look up topics and see some examples of possibilities that you could then try to reproduce by other means. | A gallery of charts, diagrams, and plot types
There is the R Graphical Manual. Although it is presumably somewhat less useful for people who use other software, you can still look up topics and see some examples of possibilities that you could t |
15,232 | A gallery of charts, diagrams, and plot types | Ralph Lengler and Martin J. Eppler's Periodic Table of Visualization Methods is an ingenious and far-ranging single-page display of about a hundred types of charts and diagrams for visualizing data as well as concepts, strategies, processes, and so on. A nice reference when looking for a catchy or creative way to di... | A gallery of charts, diagrams, and plot types | Ralph Lengler and Martin J. Eppler's Periodic Table of Visualization Methods is an ingenious and far-ranging single-page display of about a hundred types of charts and diagrams for visualizing data | A gallery of charts, diagrams, and plot types
Ralph Lengler and Martin J. Eppler's Periodic Table of Visualization Methods is an ingenious and far-ranging single-page display of about a hundred types of charts and diagrams for visualizing data as well as concepts, strategies, processes, and so on. A nice reference w... | A gallery of charts, diagrams, and plot types
Ralph Lengler and Martin J. Eppler's Periodic Table of Visualization Methods is an ingenious and far-ranging single-page display of about a hundred types of charts and diagrams for visualizing data |
15,233 | A gallery of charts, diagrams, and plot types | For a good overview of different types of plots (and examples good and bad), including a timeline of graphical development, there is plenty to explore here:
http://www.datavis.ca/gallery/ | A gallery of charts, diagrams, and plot types | For a good overview of different types of plots (and examples good and bad), including a timeline of graphical development, there is plenty to explore here:
http://www.datavis.ca/gallery/ | A gallery of charts, diagrams, and plot types
For a good overview of different types of plots (and examples good and bad), including a timeline of graphical development, there is plenty to explore here:
http://www.datavis.ca/gallery/ | A gallery of charts, diagrams, and plot types
For a good overview of different types of plots (and examples good and bad), including a timeline of graphical development, there is plenty to explore here:
http://www.datavis.ca/gallery/ |
15,234 | A gallery of charts, diagrams, and plot types | I would like to propose the R graph gallery. It displays more than hundred graphics, all made with the R software, and always giving the associated code to make it reproducible ! | A gallery of charts, diagrams, and plot types | I would like to propose the R graph gallery. It displays more than hundred graphics, all made with the R software, and always giving the associated code to make it reproducible ! | A gallery of charts, diagrams, and plot types
I would like to propose the R graph gallery. It displays more than hundred graphics, all made with the R software, and always giving the associated code to make it reproducible ! | A gallery of charts, diagrams, and plot types
I would like to propose the R graph gallery. It displays more than hundred graphics, all made with the R software, and always giving the associated code to make it reproducible ! |
15,235 | A gallery of charts, diagrams, and plot types | There's an excellent gallery from UBC Statistics dept made by an undergraduate research student.
You can preview with Shiny here, or get full code and fork on GitHub.
It's currently my go-to resource for ease of use, like picking a graph off a shelf, selection on the basis of whether it's "recommended", pointing out wh... | A gallery of charts, diagrams, and plot types | There's an excellent gallery from UBC Statistics dept made by an undergraduate research student.
You can preview with Shiny here, or get full code and fork on GitHub.
It's currently my go-to resource | A gallery of charts, diagrams, and plot types
There's an excellent gallery from UBC Statistics dept made by an undergraduate research student.
You can preview with Shiny here, or get full code and fork on GitHub.
It's currently my go-to resource for ease of use, like picking a graph off a shelf, selection on the basis ... | A gallery of charts, diagrams, and plot types
There's an excellent gallery from UBC Statistics dept made by an undergraduate research student.
You can preview with Shiny here, or get full code and fork on GitHub.
It's currently my go-to resource |
15,236 | A gallery of charts, diagrams, and plot types | Highcharts and highstocks, something like d3, might also give you lots of inspiration. Consider for example http://www.highcharts.com/demo/polar-wind-rose. On the left of that page, you can click around in their library of graphs. | A gallery of charts, diagrams, and plot types | Highcharts and highstocks, something like d3, might also give you lots of inspiration. Consider for example http://www.highcharts.com/demo/polar-wind-rose. On the left of that page, you can click arou | A gallery of charts, diagrams, and plot types
Highcharts and highstocks, something like d3, might also give you lots of inspiration. Consider for example http://www.highcharts.com/demo/polar-wind-rose. On the left of that page, you can click around in their library of graphs. | A gallery of charts, diagrams, and plot types
Highcharts and highstocks, something like d3, might also give you lots of inspiration. Consider for example http://www.highcharts.com/demo/polar-wind-rose. On the left of that page, you can click arou |
15,237 | A gallery of charts, diagrams, and plot types | Coming from a python environment I can recommend:
http://matplotlib.org/gallery.html
http://bokeh.pydata.org/en/latest/docs/gallery.html | A gallery of charts, diagrams, and plot types | Coming from a python environment I can recommend:
http://matplotlib.org/gallery.html
http://bokeh.pydata.org/en/latest/docs/gallery.html | A gallery of charts, diagrams, and plot types
Coming from a python environment I can recommend:
http://matplotlib.org/gallery.html
http://bokeh.pydata.org/en/latest/docs/gallery.html | A gallery of charts, diagrams, and plot types
Coming from a python environment I can recommend:
http://matplotlib.org/gallery.html
http://bokeh.pydata.org/en/latest/docs/gallery.html |
15,238 | Is logistic regression a non-parametric test? | Larry Wasserman defines a parametric model as a set of distributions "that can be parameterized by a finite number of parameters." (p.87) In contrast a nonparametric model is a set of distributions that cannot be paramterised by a finite number of parameters.
Thus, by that definition standard logistic regression is a p... | Is logistic regression a non-parametric test? | Larry Wasserman defines a parametric model as a set of distributions "that can be parameterized by a finite number of parameters." (p.87) In contrast a nonparametric model is a set of distributions th | Is logistic regression a non-parametric test?
Larry Wasserman defines a parametric model as a set of distributions "that can be parameterized by a finite number of parameters." (p.87) In contrast a nonparametric model is a set of distributions that cannot be paramterised by a finite number of parameters.
Thus, by that ... | Is logistic regression a non-parametric test?
Larry Wasserman defines a parametric model as a set of distributions "that can be parameterized by a finite number of parameters." (p.87) In contrast a nonparametric model is a set of distributions th |
15,239 | Is logistic regression a non-parametric test? | I'd say logistic regression isn't a test at all; however a logistic regression may then lead to no tests or several tests.
You're quite correct that labelling something nonparametric because it's not normal is insufficient. I'd call the exponential family explicitly parametric, so I'd usually regard logistic regression... | Is logistic regression a non-parametric test? | I'd say logistic regression isn't a test at all; however a logistic regression may then lead to no tests or several tests.
You're quite correct that labelling something nonparametric because it's not | Is logistic regression a non-parametric test?
I'd say logistic regression isn't a test at all; however a logistic regression may then lead to no tests or several tests.
You're quite correct that labelling something nonparametric because it's not normal is insufficient. I'd call the exponential family explicitly paramet... | Is logistic regression a non-parametric test?
I'd say logistic regression isn't a test at all; however a logistic regression may then lead to no tests or several tests.
You're quite correct that labelling something nonparametric because it's not |
15,240 | Is logistic regression a non-parametric test? | One helpful distinction that might add a little to the answers above: Andrew Ng gives a heuristic for what it means to be a non-parametric model in Lecture 1 from the course materials for Stanford's CS-229 course on machine learning.
There Ng says (pp. 14-15):
Locally weighted linear regression is the first example we... | Is logistic regression a non-parametric test? | One helpful distinction that might add a little to the answers above: Andrew Ng gives a heuristic for what it means to be a non-parametric model in Lecture 1 from the course materials for Stanford's C | Is logistic regression a non-parametric test?
One helpful distinction that might add a little to the answers above: Andrew Ng gives a heuristic for what it means to be a non-parametric model in Lecture 1 from the course materials for Stanford's CS-229 course on machine learning.
There Ng says (pp. 14-15):
Locally weig... | Is logistic regression a non-parametric test?
One helpful distinction that might add a little to the answers above: Andrew Ng gives a heuristic for what it means to be a non-parametric model in Lecture 1 from the course materials for Stanford's C |
15,241 | Is logistic regression a non-parametric test? | I think logistic regression is a parametric technique.
This might be helpful, from Wolfowitz (1942) [Additive Partition Functions and a Class of Statistical Hypotheses The Annals of Mathematical Statistics, 1942, 13, 247-279]:
“the distribution functions [note: plural!!!] of the various stochastic variables which ente... | Is logistic regression a non-parametric test? | I think logistic regression is a parametric technique.
This might be helpful, from Wolfowitz (1942) [Additive Partition Functions and a Class of Statistical Hypotheses The Annals of Mathematical Stati | Is logistic regression a non-parametric test?
I think logistic regression is a parametric technique.
This might be helpful, from Wolfowitz (1942) [Additive Partition Functions and a Class of Statistical Hypotheses The Annals of Mathematical Statistics, 1942, 13, 247-279]:
“the distribution functions [note: plural!!!] ... | Is logistic regression a non-parametric test?
I think logistic regression is a parametric technique.
This might be helpful, from Wolfowitz (1942) [Additive Partition Functions and a Class of Statistical Hypotheses The Annals of Mathematical Stati |
15,242 | Is logistic regression a non-parametric test? | Hastie and Tibshirani defines that linear regression is a parametric approach since it assumes a linear functional form of f(X). Non-parametric methods do not explicitly assume the form for f(X). This means that a non-parametric method will fit the model based on an estimate of f, calculated from the model. Logistic r... | Is logistic regression a non-parametric test? | Hastie and Tibshirani defines that linear regression is a parametric approach since it assumes a linear functional form of f(X). Non-parametric methods do not explicitly assume the form for f(X). This | Is logistic regression a non-parametric test?
Hastie and Tibshirani defines that linear regression is a parametric approach since it assumes a linear functional form of f(X). Non-parametric methods do not explicitly assume the form for f(X). This means that a non-parametric method will fit the model based on an estimat... | Is logistic regression a non-parametric test?
Hastie and Tibshirani defines that linear regression is a parametric approach since it assumes a linear functional form of f(X). Non-parametric methods do not explicitly assume the form for f(X). This |
15,243 | Factor analysis of questionnaires composed of Likert items | From what I've seen so far, FA is used for attitude items as it is for other kind of rating scales. The problem arising from the metric used (that is, "are Likert scales really to be treated as numeric scales?" is a long-standing debate, but providing you check for the bell-shaped response distribution you may handle t... | Factor analysis of questionnaires composed of Likert items | From what I've seen so far, FA is used for attitude items as it is for other kind of rating scales. The problem arising from the metric used (that is, "are Likert scales really to be treated as numeri | Factor analysis of questionnaires composed of Likert items
From what I've seen so far, FA is used for attitude items as it is for other kind of rating scales. The problem arising from the metric used (that is, "are Likert scales really to be treated as numeric scales?" is a long-standing debate, but providing you check... | Factor analysis of questionnaires composed of Likert items
From what I've seen so far, FA is used for attitude items as it is for other kind of rating scales. The problem arising from the metric used (that is, "are Likert scales really to be treated as numeri |
15,244 | Factor analysis of questionnaires composed of Likert items | Exploratory factor analysis (EFA) is appropriate (psychometrically and otherwise) for examining the extent to which one may explain correlations among multiple items by inferring the common influence of (an) unmeasured (i.e., latent) factor(s). If this is not your specific intent, consider alternative analyses, e.g.:
... | Factor analysis of questionnaires composed of Likert items | Exploratory factor analysis (EFA) is appropriate (psychometrically and otherwise) for examining the extent to which one may explain correlations among multiple items by inferring the common influence | Factor analysis of questionnaires composed of Likert items
Exploratory factor analysis (EFA) is appropriate (psychometrically and otherwise) for examining the extent to which one may explain correlations among multiple items by inferring the common influence of (an) unmeasured (i.e., latent) factor(s). If this is not ... | Factor analysis of questionnaires composed of Likert items
Exploratory factor analysis (EFA) is appropriate (psychometrically and otherwise) for examining the extent to which one may explain correlations among multiple items by inferring the common influence |
15,245 | Factor analysis of questionnaires composed of Likert items | Just a short note that you might want to look at polychoric correlation with factor analysis rather than the traditional correlation/covariance matrix.
http://www.john-uebersax.com/stat/sem.htm | Factor analysis of questionnaires composed of Likert items | Just a short note that you might want to look at polychoric correlation with factor analysis rather than the traditional correlation/covariance matrix.
http://www.john-uebersax.com/stat/sem.htm | Factor analysis of questionnaires composed of Likert items
Just a short note that you might want to look at polychoric correlation with factor analysis rather than the traditional correlation/covariance matrix.
http://www.john-uebersax.com/stat/sem.htm | Factor analysis of questionnaires composed of Likert items
Just a short note that you might want to look at polychoric correlation with factor analysis rather than the traditional correlation/covariance matrix.
http://www.john-uebersax.com/stat/sem.htm |
15,246 | Should the y-axis on a survival plot go from 0 to 100 even if the lines are all above 0.9? | Bar charts should start at 0*, because the lengths of the bars (distances from top to bottom of each bar) are what your eye compares.
But for line charts (whether survival or any other), there's no hard and fast rule. It depends on the primary goal of the chart:
If the goal is to show that the vast majority survive, k... | Should the y-axis on a survival plot go from 0 to 100 even if the lines are all above 0.9? | Bar charts should start at 0*, because the lengths of the bars (distances from top to bottom of each bar) are what your eye compares.
But for line charts (whether survival or any other), there's no ha | Should the y-axis on a survival plot go from 0 to 100 even if the lines are all above 0.9?
Bar charts should start at 0*, because the lengths of the bars (distances from top to bottom of each bar) are what your eye compares.
But for line charts (whether survival or any other), there's no hard and fast rule. It depends ... | Should the y-axis on a survival plot go from 0 to 100 even if the lines are all above 0.9?
Bar charts should start at 0*, because the lengths of the bars (distances from top to bottom of each bar) are what your eye compares.
But for line charts (whether survival or any other), there's no ha |
15,247 | Should the y-axis on a survival plot go from 0 to 100 even if the lines are all above 0.9? | I have seen it both ways. It depends on the message you are trying to send. It's true that non-statisticians (and some statisticians) are likely to be confused and miss it if the Y-axis starts at anything other than 0. In a huge study, you can have curves that barely cross 95% survival with hazard ratios of astounding ... | Should the y-axis on a survival plot go from 0 to 100 even if the lines are all above 0.9? | I have seen it both ways. It depends on the message you are trying to send. It's true that non-statisticians (and some statisticians) are likely to be confused and miss it if the Y-axis starts at anyt | Should the y-axis on a survival plot go from 0 to 100 even if the lines are all above 0.9?
I have seen it both ways. It depends on the message you are trying to send. It's true that non-statisticians (and some statisticians) are likely to be confused and miss it if the Y-axis starts at anything other than 0. In a huge ... | Should the y-axis on a survival plot go from 0 to 100 even if the lines are all above 0.9?
I have seen it both ways. It depends on the message you are trying to send. It's true that non-statisticians (and some statisticians) are likely to be confused and miss it if the Y-axis starts at anyt |
15,248 | Should the y-axis on a survival plot go from 0 to 100 even if the lines are all above 0.9? | Generally, starting the graph above zero is preferable, as it allows the viewer to more easily digest the information, which is the point of the graph. You do have to be careful to not mislead the viewer into such misperceptions as that twice the distance from the bottom means twice the value. You can emphasize that i... | Should the y-axis on a survival plot go from 0 to 100 even if the lines are all above 0.9? | Generally, starting the graph above zero is preferable, as it allows the viewer to more easily digest the information, which is the point of the graph. You do have to be careful to not mislead the vi | Should the y-axis on a survival plot go from 0 to 100 even if the lines are all above 0.9?
Generally, starting the graph above zero is preferable, as it allows the viewer to more easily digest the information, which is the point of the graph. You do have to be careful to not mislead the viewer into such misperceptions... | Should the y-axis on a survival plot go from 0 to 100 even if the lines are all above 0.9?
Generally, starting the graph above zero is preferable, as it allows the viewer to more easily digest the information, which is the point of the graph. You do have to be careful to not mislead the vi |
15,249 | Can $\sin(x)$ be used as activation in deep learning? | Here's a paper dedicated to this very question:
Parascandolo and Virtanen (2016). Taming the waves: sine as activation function in deep neural networks.
Some key points from the paper:
Sinusoidal activation functions have been largely ignored, and are considered difficult to train.
They review past work that has use... | Can $\sin(x)$ be used as activation in deep learning? | Here's a paper dedicated to this very question:
Parascandolo and Virtanen (2016). Taming the waves: sine as activation function in deep neural networks.
Some key points from the paper:
Sinusoidal a | Can $\sin(x)$ be used as activation in deep learning?
Here's a paper dedicated to this very question:
Parascandolo and Virtanen (2016). Taming the waves: sine as activation function in deep neural networks.
Some key points from the paper:
Sinusoidal activation functions have been largely ignored, and are considered ... | Can $\sin(x)$ be used as activation in deep learning?
Here's a paper dedicated to this very question:
Parascandolo and Virtanen (2016). Taming the waves: sine as activation function in deep neural networks.
Some key points from the paper:
Sinusoidal a |
15,250 | Can $\sin(x)$ be used as activation in deep learning? | As of June 2020, work from Stanford has demonstrated that your intuition was right and that $sin(x)$ can be indeed used for a variety of tasks effectively:
https://vsitzmann.github.io/siren/
https://arxiv.org/pdf/2006.09661.pdf | Can $\sin(x)$ be used as activation in deep learning? | As of June 2020, work from Stanford has demonstrated that your intuition was right and that $sin(x)$ can be indeed used for a variety of tasks effectively:
https://vsitzmann.github.io/siren/
https:// | Can $\sin(x)$ be used as activation in deep learning?
As of June 2020, work from Stanford has demonstrated that your intuition was right and that $sin(x)$ can be indeed used for a variety of tasks effectively:
https://vsitzmann.github.io/siren/
https://arxiv.org/pdf/2006.09661.pdf | Can $\sin(x)$ be used as activation in deep learning?
As of June 2020, work from Stanford has demonstrated that your intuition was right and that $sin(x)$ can be indeed used for a variety of tasks effectively:
https://vsitzmann.github.io/siren/
https:// |
15,251 | Can $\sin(x)$ be used as activation in deep learning? | It surely "can" be used. The fact that it is not being used1, however, suggests that it is not very much practical. The gradient of $\sin$ is actually zero at $\frac \pi 2+k\pi$ for any integer $k$. I think the main problem with using $\sin$ activation is that it introduces infinitely many symmetries, which may make th... | Can $\sin(x)$ be used as activation in deep learning? | It surely "can" be used. The fact that it is not being used1, however, suggests that it is not very much practical. The gradient of $\sin$ is actually zero at $\frac \pi 2+k\pi$ for any integer $k$. I | Can $\sin(x)$ be used as activation in deep learning?
It surely "can" be used. The fact that it is not being used1, however, suggests that it is not very much practical. The gradient of $\sin$ is actually zero at $\frac \pi 2+k\pi$ for any integer $k$. I think the main problem with using $\sin$ activation is that it in... | Can $\sin(x)$ be used as activation in deep learning?
It surely "can" be used. The fact that it is not being used1, however, suggests that it is not very much practical. The gradient of $\sin$ is actually zero at $\frac \pi 2+k\pi$ for any integer $k$. I |
15,252 | Can $\sin(x)$ be used as activation in deep learning? | This was a part of my master thesis. I have researched and mentioned previous publications on the use of sine function as activation and as the basis function. In addition, I have designed several different versions of the sine function and used them as the basis function in the artificial neural networks as a replacem... | Can $\sin(x)$ be used as activation in deep learning? | This was a part of my master thesis. I have researched and mentioned previous publications on the use of sine function as activation and as the basis function. In addition, I have designed several dif | Can $\sin(x)$ be used as activation in deep learning?
This was a part of my master thesis. I have researched and mentioned previous publications on the use of sine function as activation and as the basis function. In addition, I have designed several different versions of the sine function and used them as the basis fu... | Can $\sin(x)$ be used as activation in deep learning?
This was a part of my master thesis. I have researched and mentioned previous publications on the use of sine function as activation and as the basis function. In addition, I have designed several dif |
15,253 | Probability that Secret Santa arrangement will result in perfect pairings | The total number of assignments among $2n$ people, where nobody is assigned to themselves, is $$d(2n) = (2n)!(1/2 - 1/6 + \cdots + (-1)^k/k! + \cdots + 1/(2n)!).$$ (These are called derangements.) The value is very close to $(2n)! / e$.
If they correspond to perfect pairings, then they are a product of disjoint trans... | Probability that Secret Santa arrangement will result in perfect pairings | The total number of assignments among $2n$ people, where nobody is assigned to themselves, is $$d(2n) = (2n)!(1/2 - 1/6 + \cdots + (-1)^k/k! + \cdots + 1/(2n)!).$$ (These are called derangements.) T | Probability that Secret Santa arrangement will result in perfect pairings
The total number of assignments among $2n$ people, where nobody is assigned to themselves, is $$d(2n) = (2n)!(1/2 - 1/6 + \cdots + (-1)^k/k! + \cdots + 1/(2n)!).$$ (These are called derangements.) The value is very close to $(2n)! / e$.
If they... | Probability that Secret Santa arrangement will result in perfect pairings
The total number of assignments among $2n$ people, where nobody is assigned to themselves, is $$d(2n) = (2n)!(1/2 - 1/6 + \cdots + (-1)^k/k! + \cdots + 1/(2n)!).$$ (These are called derangements.) T |
15,254 | Probability that Secret Santa arrangement will result in perfect pairings | I was quite impressed by the elegance in @whuber answer. To be honest I had to do a lot of acquainting myself with new concepts to follow the steps in his solution. After spending a lot of time on it, I've decided to post what I got. So what follows is an exegetical note to his already accepted response. In this way th... | Probability that Secret Santa arrangement will result in perfect pairings | I was quite impressed by the elegance in @whuber answer. To be honest I had to do a lot of acquainting myself with new concepts to follow the steps in his solution. After spending a lot of time on it, | Probability that Secret Santa arrangement will result in perfect pairings
I was quite impressed by the elegance in @whuber answer. To be honest I had to do a lot of acquainting myself with new concepts to follow the steps in his solution. After spending a lot of time on it, I've decided to post what I got. So what foll... | Probability that Secret Santa arrangement will result in perfect pairings
I was quite impressed by the elegance in @whuber answer. To be honest I had to do a lot of acquainting myself with new concepts to follow the steps in his solution. After spending a lot of time on it, |
15,255 | Probability that Secret Santa arrangement will result in perfect pairings | So this answer is prompted by a few questions in the comments. The earlier two answers are correct if the implicit assumption that all derangements are equally likely is true. This is sadly not the case with the method actually used in the question which is why quite a lot of secret santas are now using computer based ... | Probability that Secret Santa arrangement will result in perfect pairings | So this answer is prompted by a few questions in the comments. The earlier two answers are correct if the implicit assumption that all derangements are equally likely is true. This is sadly not the ca | Probability that Secret Santa arrangement will result in perfect pairings
So this answer is prompted by a few questions in the comments. The earlier two answers are correct if the implicit assumption that all derangements are equally likely is true. This is sadly not the case with the method actually used in the questi... | Probability that Secret Santa arrangement will result in perfect pairings
So this answer is prompted by a few questions in the comments. The earlier two answers are correct if the implicit assumption that all derangements are equally likely is true. This is sadly not the ca |
15,256 | Probability that Secret Santa arrangement will result in perfect pairings | As @Juho-Kokkala and others have stated, the valid derangements are not equally likely and it is not stated in the problem what happens if the last person (H in the example) is the same as the last name in the bowl. I think it is understood from the question what happens when a person prior to the last one draws their ... | Probability that Secret Santa arrangement will result in perfect pairings | As @Juho-Kokkala and others have stated, the valid derangements are not equally likely and it is not stated in the problem what happens if the last person (H in the example) is the same as the last na | Probability that Secret Santa arrangement will result in perfect pairings
As @Juho-Kokkala and others have stated, the valid derangements are not equally likely and it is not stated in the problem what happens if the last person (H in the example) is the same as the last name in the bowl. I think it is understood from ... | Probability that Secret Santa arrangement will result in perfect pairings
As @Juho-Kokkala and others have stated, the valid derangements are not equally likely and it is not stated in the problem what happens if the last person (H in the example) is the same as the last na |
15,257 | R Language what is difference between rnorm and runif [closed] | rnorm generates a random value from the normal distribution. runif generates a random value from the uniform. | R Language what is difference between rnorm and runif [closed] | rnorm generates a random value from the normal distribution. runif generates a random value from the uniform. | R Language what is difference between rnorm and runif [closed]
rnorm generates a random value from the normal distribution. runif generates a random value from the uniform. | R Language what is difference between rnorm and runif [closed]
rnorm generates a random value from the normal distribution. runif generates a random value from the uniform. |
15,258 | R Language what is difference between rnorm and runif [closed] | rnorm(n, mean = , sd = ) is used to generate n normal random numbers with arguments mean and sd; while runif(n, min = , max = ) is used to generate n uniform random numbers lie in the interval (min, max).
Please check corresponding R help documents for details. | R Language what is difference between rnorm and runif [closed] | rnorm(n, mean = , sd = ) is used to generate n normal random numbers with arguments mean and sd; while runif(n, min = , max = ) is used to generate n uniform random numbers lie in the interval (min, m | R Language what is difference between rnorm and runif [closed]
rnorm(n, mean = , sd = ) is used to generate n normal random numbers with arguments mean and sd; while runif(n, min = , max = ) is used to generate n uniform random numbers lie in the interval (min, max).
Please check corresponding R help documents for de... | R Language what is difference between rnorm and runif [closed]
rnorm(n, mean = , sd = ) is used to generate n normal random numbers with arguments mean and sd; while runif(n, min = , max = ) is used to generate n uniform random numbers lie in the interval (min, m |
15,259 | Relationships between correlation and causation | "Conditioning" is a word from probability theory : https://en.wikipedia.org/wiki/Conditional_probability
Conditioning on C means that we are only looking at cases where C is true. "Implicitly" means that we may not be making this restriction explicit, sometimes not even aware of doing it.
The point means that, when A a... | Relationships between correlation and causation | "Conditioning" is a word from probability theory : https://en.wikipedia.org/wiki/Conditional_probability
Conditioning on C means that we are only looking at cases where C is true. "Implicitly" means t | Relationships between correlation and causation
"Conditioning" is a word from probability theory : https://en.wikipedia.org/wiki/Conditional_probability
Conditioning on C means that we are only looking at cases where C is true. "Implicitly" means that we may not be making this restriction explicit, sometimes not even a... | Relationships between correlation and causation
"Conditioning" is a word from probability theory : https://en.wikipedia.org/wiki/Conditional_probability
Conditioning on C means that we are only looking at cases where C is true. "Implicitly" means t |
15,260 | Relationships between correlation and causation | The fourth point is an example of Berkson's paradox, also known as conditioning on a collider, also known as the explaining-away phenomenon.
As an example, consider a young woman who is frequently asked on dates by young men, and she must decide whether to accept or reject each date proposal. The young men vary in how ... | Relationships between correlation and causation | The fourth point is an example of Berkson's paradox, also known as conditioning on a collider, also known as the explaining-away phenomenon.
As an example, consider a young woman who is frequently ask | Relationships between correlation and causation
The fourth point is an example of Berkson's paradox, also known as conditioning on a collider, also known as the explaining-away phenomenon.
As an example, consider a young woman who is frequently asked on dates by young men, and she must decide whether to accept or rejec... | Relationships between correlation and causation
The fourth point is an example of Berkson's paradox, also known as conditioning on a collider, also known as the explaining-away phenomenon.
As an example, consider a young woman who is frequently ask |
15,261 | Relationships between correlation and causation | Simpson's paradox and Berkson's paradox can each give examples of "A and B both cause C, which is (explicitly or implicitly) conditioned on"
As an example suppose I have $1000$ stamps in my collection of which $100$ are rare ($10\%$) and $200$ are pretty ($20\%$). If there is no intrinsic relationship between rarity a... | Relationships between correlation and causation | Simpson's paradox and Berkson's paradox can each give examples of "A and B both cause C, which is (explicitly or implicitly) conditioned on"
As an example suppose I have $1000$ stamps in my collection | Relationships between correlation and causation
Simpson's paradox and Berkson's paradox can each give examples of "A and B both cause C, which is (explicitly or implicitly) conditioned on"
As an example suppose I have $1000$ stamps in my collection of which $100$ are rare ($10\%$) and $200$ are pretty ($20\%$). If the... | Relationships between correlation and causation
Simpson's paradox and Berkson's paradox can each give examples of "A and B both cause C, which is (explicitly or implicitly) conditioned on"
As an example suppose I have $1000$ stamps in my collection |
15,262 | Relationships between correlation and causation | The paragraph starts with "For any two correlated events, A and B,...", so my guess is that correlation is assumed at the beginning. In other words, they need not be correlated to simultaneously cause C, but if they were correlated and they did both cause C, it does not imply that there exists a causal relationship bet... | Relationships between correlation and causation | The paragraph starts with "For any two correlated events, A and B,...", so my guess is that correlation is assumed at the beginning. In other words, they need not be correlated to simultaneously cause | Relationships between correlation and causation
The paragraph starts with "For any two correlated events, A and B,...", so my guess is that correlation is assumed at the beginning. In other words, they need not be correlated to simultaneously cause C, but if they were correlated and they did both cause C, it does not i... | Relationships between correlation and causation
The paragraph starts with "For any two correlated events, A and B,...", so my guess is that correlation is assumed at the beginning. In other words, they need not be correlated to simultaneously cause |
15,263 | Calculate coefficients in a logistic regression with R | The OLS estimator in the linear regression model is quite rare in
having the property that it can be represented in closed form, that is
without needing to be expressed as the optimizer of a function. It is, however, an optimizer of a function -- the residual sum of squares
function -- and can be computed as such.
Th... | Calculate coefficients in a logistic regression with R | The OLS estimator in the linear regression model is quite rare in
having the property that it can be represented in closed form, that is
without needing to be expressed as the optimizer of a function | Calculate coefficients in a logistic regression with R
The OLS estimator in the linear regression model is quite rare in
having the property that it can be represented in closed form, that is
without needing to be expressed as the optimizer of a function. It is, however, an optimizer of a function -- the residual sum ... | Calculate coefficients in a logistic regression with R
The OLS estimator in the linear regression model is quite rare in
having the property that it can be represented in closed form, that is
without needing to be expressed as the optimizer of a function |
15,264 | Calculate coefficients in a logistic regression with R | You can't get there from here. The solutions to both the general linear model and the logistic model arise from solving the respective maximum likelihood equations, but only the linear model has a closed form solution.
If you consult McCullagh and Nelder's book, you can learn how the solutions are obtained in the logis... | Calculate coefficients in a logistic regression with R | You can't get there from here. The solutions to both the general linear model and the logistic model arise from solving the respective maximum likelihood equations, but only the linear model has a clo | Calculate coefficients in a logistic regression with R
You can't get there from here. The solutions to both the general linear model and the logistic model arise from solving the respective maximum likelihood equations, but only the linear model has a closed form solution.
If you consult McCullagh and Nelder's book, yo... | Calculate coefficients in a logistic regression with R
You can't get there from here. The solutions to both the general linear model and the logistic model arise from solving the respective maximum likelihood equations, but only the linear model has a clo |
15,265 | Why can't I match glmer (family=binomial) output with manual implementation of Gauss-Newton algorithm? | If you change your model fitting command to the following, your matching
approach works:
my.lmer <- glmer(y ~ x1 + (1 | subject), data = df, family = binomial, nAGQ = 0)
The key change is the nAGQ = 0, which matches your approach, whereas the
default (nAGQ = 1) does not. nAGQ means 'number of adaptive
Gauss-Hermite q... | Why can't I match glmer (family=binomial) output with manual implementation of Gauss-Newton algorith | If you change your model fitting command to the following, your matching
approach works:
my.lmer <- glmer(y ~ x1 + (1 | subject), data = df, family = binomial, nAGQ = 0)
The key change is the nAGQ = | Why can't I match glmer (family=binomial) output with manual implementation of Gauss-Newton algorithm?
If you change your model fitting command to the following, your matching
approach works:
my.lmer <- glmer(y ~ x1 + (1 | subject), data = df, family = binomial, nAGQ = 0)
The key change is the nAGQ = 0, which matches ... | Why can't I match glmer (family=binomial) output with manual implementation of Gauss-Newton algorith
If you change your model fitting command to the following, your matching
approach works:
my.lmer <- glmer(y ~ x1 + (1 | subject), data = df, family = binomial, nAGQ = 0)
The key change is the nAGQ = |
15,266 | Converting standardized betas back to original variables | For the regression model using the standardized variables, we assume the following form for the regression line
$$
\mathbb E[Y] =\beta_{0}+\sum_{j=1}^{k}\beta_{j}z_{j},
$$
where $z_{j}$ is the j-th (standardized) regressor, generated from $x_j$ by subtracting the sample mean $\bar x_j$ and dividing by the sample standa... | Converting standardized betas back to original variables | For the regression model using the standardized variables, we assume the following form for the regression line
$$
\mathbb E[Y] =\beta_{0}+\sum_{j=1}^{k}\beta_{j}z_{j},
$$
where $z_{j}$ is the j-th (s | Converting standardized betas back to original variables
For the regression model using the standardized variables, we assume the following form for the regression line
$$
\mathbb E[Y] =\beta_{0}+\sum_{j=1}^{k}\beta_{j}z_{j},
$$
where $z_{j}$ is the j-th (standardized) regressor, generated from $x_j$ by subtracting the... | Converting standardized betas back to original variables
For the regression model using the standardized variables, we assume the following form for the regression line
$$
\mathbb E[Y] =\beta_{0}+\sum_{j=1}^{k}\beta_{j}z_{j},
$$
where $z_{j}$ is the j-th (s |
15,267 | Converting standardized betas back to original variables | If you standardize both the regressor matrix and the response vector then the intercept vector is zero. See proof:
https://www.statlect.com/fundamentals-of-statistics/linear-regression-with-standardized-variables
So the assumed form for the regression line is incorrect for the standardized case.
Nevertheless, the deriv... | Converting standardized betas back to original variables | If you standardize both the regressor matrix and the response vector then the intercept vector is zero. See proof:
https://www.statlect.com/fundamentals-of-statistics/linear-regression-with-standardiz | Converting standardized betas back to original variables
If you standardize both the regressor matrix and the response vector then the intercept vector is zero. See proof:
https://www.statlect.com/fundamentals-of-statistics/linear-regression-with-standardized-variables
So the assumed form for the regression line is inc... | Converting standardized betas back to original variables
If you standardize both the regressor matrix and the response vector then the intercept vector is zero. See proof:
https://www.statlect.com/fundamentals-of-statistics/linear-regression-with-standardiz |
15,268 | Does this quantity related to independence have a name? | It's observed to expected ratio (abbreviation: o/e).
Quoting an answer to About joint probability divided by the product of the probabilities at Math.SE (pointed out by Procrastinator):
Then, at least in the environmental, medical and life sciences literature, P(A∩B)/(P(A)P(B)) is called the observed to expected rati... | Does this quantity related to independence have a name? | It's observed to expected ratio (abbreviation: o/e).
Quoting an answer to About joint probability divided by the product of the probabilities at Math.SE (pointed out by Procrastinator):
Then, at lea | Does this quantity related to independence have a name?
It's observed to expected ratio (abbreviation: o/e).
Quoting an answer to About joint probability divided by the product of the probabilities at Math.SE (pointed out by Procrastinator):
Then, at least in the environmental, medical and life sciences literature, P... | Does this quantity related to independence have a name?
It's observed to expected ratio (abbreviation: o/e).
Quoting an answer to About joint probability divided by the product of the probabilities at Math.SE (pointed out by Procrastinator):
Then, at lea |
15,269 | Does this quantity related to independence have a name? | I think that you are looking for Lift (or improvement). Lift is the ratio of the probability that A and B occur together to the multiple of the two individual probabilities for A and B. It is used to interpret the importance of a rule in association rule mining. Lift is a way to measure how much better a model is over ... | Does this quantity related to independence have a name? | I think that you are looking for Lift (or improvement). Lift is the ratio of the probability that A and B occur together to the multiple of the two individual probabilities for A and B. It is used to | Does this quantity related to independence have a name?
I think that you are looking for Lift (or improvement). Lift is the ratio of the probability that A and B occur together to the multiple of the two individual probabilities for A and B. It is used to interpret the importance of a rule in association rule mining. L... | Does this quantity related to independence have a name?
I think that you are looking for Lift (or improvement). Lift is the ratio of the probability that A and B occur together to the multiple of the two individual probabilities for A and B. It is used to |
15,270 | Does this quantity related to independence have a name? | The correspondence analysis folk call one of these quantities a contingency ratio, in the context of cross-tabulated counts. The distances of multiple such ratios from 1 are what biplots visualize. See e.g. Greenacre (1993) ch.13.
The old-school machine learning feature selection folk call the log of this quantity po... | Does this quantity related to independence have a name? | The correspondence analysis folk call one of these quantities a contingency ratio, in the context of cross-tabulated counts. The distances of multiple such ratios from 1 are what biplots visualize. | Does this quantity related to independence have a name?
The correspondence analysis folk call one of these quantities a contingency ratio, in the context of cross-tabulated counts. The distances of multiple such ratios from 1 are what biplots visualize. See e.g. Greenacre (1993) ch.13.
The old-school machine learning... | Does this quantity related to independence have a name?
The correspondence analysis folk call one of these quantities a contingency ratio, in the context of cross-tabulated counts. The distances of multiple such ratios from 1 are what biplots visualize. |
15,271 | Does this quantity related to independence have a name? | In Data Mining it seems they call this lift. | Does this quantity related to independence have a name? | In Data Mining it seems they call this lift. | Does this quantity related to independence have a name?
In Data Mining it seems they call this lift. | Does this quantity related to independence have a name?
In Data Mining it seems they call this lift. |
15,272 | Does this quantity related to independence have a name? | Maybe you are asking how this quantity is related to the Odds Ratio,
as a quantity for measuring independence.
I think you are searching for "Relation to statistical independence". See http://en.wikipedia.org/wiki/Odds_ratio | Does this quantity related to independence have a name? | Maybe you are asking how this quantity is related to the Odds Ratio,
as a quantity for measuring independence.
I think you are searching for "Relation to statistical independence". See http://en.wikip | Does this quantity related to independence have a name?
Maybe you are asking how this quantity is related to the Odds Ratio,
as a quantity for measuring independence.
I think you are searching for "Relation to statistical independence". See http://en.wikipedia.org/wiki/Odds_ratio | Does this quantity related to independence have a name?
Maybe you are asking how this quantity is related to the Odds Ratio,
as a quantity for measuring independence.
I think you are searching for "Relation to statistical independence". See http://en.wikip |
15,273 | What is the difference between statistics and biostatistics? | When I look at the Wikipedia entry for biostatistics, the relation to biometrics doesn't seem so obvious to me since, historically, biometrics was more concerned with characterizing individuals by some phenotypes of interest, with large applications in population genetics (as exemplified by the work of Fisher), whereas... | What is the difference between statistics and biostatistics? | When I look at the Wikipedia entry for biostatistics, the relation to biometrics doesn't seem so obvious to me since, historically, biometrics was more concerned with characterizing individuals by som | What is the difference between statistics and biostatistics?
When I look at the Wikipedia entry for biostatistics, the relation to biometrics doesn't seem so obvious to me since, historically, biometrics was more concerned with characterizing individuals by some phenotypes of interest, with large applications in popula... | What is the difference between statistics and biostatistics?
When I look at the Wikipedia entry for biostatistics, the relation to biometrics doesn't seem so obvious to me since, historically, biometrics was more concerned with characterizing individuals by som |
15,274 | What is the difference between statistics and biostatistics? | To quote the "Encyclopedic dictionary of mathematics" by Kiyosi Itô (ed.):
In many applied fields there exist systems of statistical methods which have been developed specifically for the respective fields, and although all of them are based essentially on the same general principles of statistical inference, each has ... | What is the difference between statistics and biostatistics? | To quote the "Encyclopedic dictionary of mathematics" by Kiyosi Itô (ed.):
In many applied fields there exist systems of statistical methods which have been developed specifically for the respective f | What is the difference between statistics and biostatistics?
To quote the "Encyclopedic dictionary of mathematics" by Kiyosi Itô (ed.):
In many applied fields there exist systems of statistical methods which have been developed specifically for the respective fields, and although all of them are based essentially on th... | What is the difference between statistics and biostatistics?
To quote the "Encyclopedic dictionary of mathematics" by Kiyosi Itô (ed.):
In many applied fields there exist systems of statistical methods which have been developed specifically for the respective f |
15,275 | What is the difference between statistics and biostatistics? | As someone who took courses from the Statistics department of a university which did not offer a Biostatistics major and worked in clinical trials with biostatisticians and read many papers written by biostatisticians, I can offer a particular perspective. I see biostatistics as a field that applies a subset of standa... | What is the difference between statistics and biostatistics? | As someone who took courses from the Statistics department of a university which did not offer a Biostatistics major and worked in clinical trials with biostatisticians and read many papers written by | What is the difference between statistics and biostatistics?
As someone who took courses from the Statistics department of a university which did not offer a Biostatistics major and worked in clinical trials with biostatisticians and read many papers written by biostatisticians, I can offer a particular perspective. I... | What is the difference between statistics and biostatistics?
As someone who took courses from the Statistics department of a university which did not offer a Biostatistics major and worked in clinical trials with biostatisticians and read many papers written by |
15,276 | What is the difference between statistics and biostatistics? | I will take a swing at answering this from the perspective of someone who is neither a statistician nor a biostatistician. Rather, I exist in the blurry grey area that is "epidemiological methods".
As other posters have mentioned, biostatistics is a discipline particularly focused on statistics as they apply to biologi... | What is the difference between statistics and biostatistics? | I will take a swing at answering this from the perspective of someone who is neither a statistician nor a biostatistician. Rather, I exist in the blurry grey area that is "epidemiological methods".
As | What is the difference between statistics and biostatistics?
I will take a swing at answering this from the perspective of someone who is neither a statistician nor a biostatistician. Rather, I exist in the blurry grey area that is "epidemiological methods".
As other posters have mentioned, biostatistics is a disciplin... | What is the difference between statistics and biostatistics?
I will take a swing at answering this from the perspective of someone who is neither a statistician nor a biostatistician. Rather, I exist in the blurry grey area that is "epidemiological methods".
As |
15,277 | What is the difference between statistics and biostatistics? | Biostatistics, biometrics and biometry are synonyms. Medical statistics (sometimes called 'clinical biostatistics' for no clear reason) is a subset of these. | What is the difference between statistics and biostatistics? | Biostatistics, biometrics and biometry are synonyms. Medical statistics (sometimes called 'clinical biostatistics' for no clear reason) is a subset of these. | What is the difference between statistics and biostatistics?
Biostatistics, biometrics and biometry are synonyms. Medical statistics (sometimes called 'clinical biostatistics' for no clear reason) is a subset of these. | What is the difference between statistics and biostatistics?
Biostatistics, biometrics and biometry are synonyms. Medical statistics (sometimes called 'clinical biostatistics' for no clear reason) is a subset of these. |
15,278 | What is the difference between statistics and biostatistics? | Statistics vs. Biostatistics does not make sense as a comparison; biostatistics is really a sub topic of statistics. This would be like asking "what's the difference between mathematics and probability?"; probability is a subfield of mathematics.
As others have noted, biostatistics applies to problems that are very co... | What is the difference between statistics and biostatistics? | Statistics vs. Biostatistics does not make sense as a comparison; biostatistics is really a sub topic of statistics. This would be like asking "what's the difference between mathematics and probabilit | What is the difference between statistics and biostatistics?
Statistics vs. Biostatistics does not make sense as a comparison; biostatistics is really a sub topic of statistics. This would be like asking "what's the difference between mathematics and probability?"; probability is a subfield of mathematics.
As others h... | What is the difference between statistics and biostatistics?
Statistics vs. Biostatistics does not make sense as a comparison; biostatistics is really a sub topic of statistics. This would be like asking "what's the difference between mathematics and probabilit |
15,279 | What is the difference between statistics and biostatistics? | I see the answers here just define the domain of work so I try to give a more comprehensive answer based on my experience of learning statistics as a medical practitioner. Most of my experience is on clinical trials, but this can be applied to any domain of biostatistics.
The purpose of biostatistics is biological and ... | What is the difference between statistics and biostatistics? | I see the answers here just define the domain of work so I try to give a more comprehensive answer based on my experience of learning statistics as a medical practitioner. Most of my experience is on | What is the difference between statistics and biostatistics?
I see the answers here just define the domain of work so I try to give a more comprehensive answer based on my experience of learning statistics as a medical practitioner. Most of my experience is on clinical trials, but this can be applied to any domain of b... | What is the difference between statistics and biostatistics?
I see the answers here just define the domain of work so I try to give a more comprehensive answer based on my experience of learning statistics as a medical practitioner. Most of my experience is on |
15,280 | What is the difference between statistics and biostatistics? | As for what I see this seems to be just a matter of semantics. Statistics applied to research or testing in the social sciences is just called Statistics. A person working with this type of situations needs to have a through knowledge of his or her field before applying a statistical procedure. Anyway we just call it S... | What is the difference between statistics and biostatistics? | As for what I see this seems to be just a matter of semantics. Statistics applied to research or testing in the social sciences is just called Statistics. A person working with this type of situations | What is the difference between statistics and biostatistics?
As for what I see this seems to be just a matter of semantics. Statistics applied to research or testing in the social sciences is just called Statistics. A person working with this type of situations needs to have a through knowledge of his or her field befo... | What is the difference between statistics and biostatistics?
As for what I see this seems to be just a matter of semantics. Statistics applied to research or testing in the social sciences is just called Statistics. A person working with this type of situations |
15,281 | What is the difference between statistics and biostatistics? | There is not a significant difference between statistics and biostatistics. In my definition, biostatistics is the application of statistics to biology. So a Biostatistician has a relatively strong command in biology, well at least enough to understand how to apply his statistics to biology.
It would be the same conce... | What is the difference between statistics and biostatistics? | There is not a significant difference between statistics and biostatistics. In my definition, biostatistics is the application of statistics to biology. So a Biostatistician has a relatively strong co | What is the difference between statistics and biostatistics?
There is not a significant difference between statistics and biostatistics. In my definition, biostatistics is the application of statistics to biology. So a Biostatistician has a relatively strong command in biology, well at least enough to understand how to... | What is the difference between statistics and biostatistics?
There is not a significant difference between statistics and biostatistics. In my definition, biostatistics is the application of statistics to biology. So a Biostatistician has a relatively strong co |
15,282 | Why was the letter Q chosen in Q-learning? | I'm sorry to disappoint everyone, but Q doesn't stand for anything :)
Q-learning was proposed by Watkins in his PhD thesis in 1989, see p.96. The Q in the equation on that page is updated in certain way at each step. The Q is the expected return from action at a given state, see the definition of Q on p.46. The return ... | Why was the letter Q chosen in Q-learning? | I'm sorry to disappoint everyone, but Q doesn't stand for anything :)
Q-learning was proposed by Watkins in his PhD thesis in 1989, see p.96. The Q in the equation on that page is updated in certain w | Why was the letter Q chosen in Q-learning?
I'm sorry to disappoint everyone, but Q doesn't stand for anything :)
Q-learning was proposed by Watkins in his PhD thesis in 1989, see p.96. The Q in the equation on that page is updated in certain way at each step. The Q is the expected return from action at a given state, s... | Why was the letter Q chosen in Q-learning?
I'm sorry to disappoint everyone, but Q doesn't stand for anything :)
Q-learning was proposed by Watkins in his PhD thesis in 1989, see p.96. The Q in the equation on that page is updated in certain w |
15,283 | Why was the letter Q chosen in Q-learning? | The reason Q-Learning is called so because it uses Q values to form it's estimates. The usual learning rule is, $Q(s_t,a_t)\gets Q(s_t,a_t)+\alpha(r_t+\gamma \times \max_{a} Q(s_{t+1},a)-Q(s_t,a_t))$ and it should be clear why it is called Q-Learning.
But the actual question in my view is why Q-Learning is called so. T... | Why was the letter Q chosen in Q-learning? | The reason Q-Learning is called so because it uses Q values to form it's estimates. The usual learning rule is, $Q(s_t,a_t)\gets Q(s_t,a_t)+\alpha(r_t+\gamma \times \max_{a} Q(s_{t+1},a)-Q(s_t,a_t))$ | Why was the letter Q chosen in Q-learning?
The reason Q-Learning is called so because it uses Q values to form it's estimates. The usual learning rule is, $Q(s_t,a_t)\gets Q(s_t,a_t)+\alpha(r_t+\gamma \times \max_{a} Q(s_{t+1},a)-Q(s_t,a_t))$ and it should be clear why it is called Q-Learning.
But the actual question i... | Why was the letter Q chosen in Q-learning?
The reason Q-Learning is called so because it uses Q values to form it's estimates. The usual learning rule is, $Q(s_t,a_t)\gets Q(s_t,a_t)+\alpha(r_t+\gamma \times \max_{a} Q(s_{t+1},a)-Q(s_t,a_t))$ |
15,284 | Google Inception model:why there is multiple softmax? | Short answer: Deep architectures, and specifically GoogLeNet (22 layers) are in danger of the vanishing gradients problem during training (back-propagation algorithm). The engineers of GoogLeNet addressed this issue by adding classifiers in the intermediate layers as well, such that the final loss is a combination of t... | Google Inception model:why there is multiple softmax? | Short answer: Deep architectures, and specifically GoogLeNet (22 layers) are in danger of the vanishing gradients problem during training (back-propagation algorithm). The engineers of GoogLeNet addre | Google Inception model:why there is multiple softmax?
Short answer: Deep architectures, and specifically GoogLeNet (22 layers) are in danger of the vanishing gradients problem during training (back-propagation algorithm). The engineers of GoogLeNet addressed this issue by adding classifiers in the intermediate layers a... | Google Inception model:why there is multiple softmax?
Short answer: Deep architectures, and specifically GoogLeNet (22 layers) are in danger of the vanishing gradients problem during training (back-propagation algorithm). The engineers of GoogLeNet addre |
15,285 | Google Inception model:why there is multiple softmax? | In addition to the answer of @galoosh33: It seems to me that the auxiliary classifiers use the same labels as the final output classifier. Source: slide 34 in https://pdfs.semanticscholar.org/0b99/d677883883584d9a328f6f2d54738363997a.pdf
Previously, I wondered whether these auxiliary classifiers used other type of lab... | Google Inception model:why there is multiple softmax? | In addition to the answer of @galoosh33: It seems to me that the auxiliary classifiers use the same labels as the final output classifier. Source: slide 34 in https://pdfs.semanticscholar.org/0b99/d67 | Google Inception model:why there is multiple softmax?
In addition to the answer of @galoosh33: It seems to me that the auxiliary classifiers use the same labels as the final output classifier. Source: slide 34 in https://pdfs.semanticscholar.org/0b99/d677883883584d9a328f6f2d54738363997a.pdf
Previously, I wondered whet... | Google Inception model:why there is multiple softmax?
In addition to the answer of @galoosh33: It seems to me that the auxiliary classifiers use the same labels as the final output classifier. Source: slide 34 in https://pdfs.semanticscholar.org/0b99/d67 |
15,286 | What is the intuition behind the independence of $X_2-X_1$ and $X_1+X_2$, $X_i \sim N(0,1)$? | This is standard normal distributed data:
Notice that the distribution is circulary symmetric.
When you switch to $Y_1 = X_2 - X_1$ and $Y_2 = X_1 + X_2$, you effectively rotate and scale the axis, like this:
This new coordinate system has the same origin as the original one, and the axis are orthogonal. Due to the ... | What is the intuition behind the independence of $X_2-X_1$ and $X_1+X_2$, $X_i \sim N(0,1)$? | This is standard normal distributed data:
Notice that the distribution is circulary symmetric.
When you switch to $Y_1 = X_2 - X_1$ and $Y_2 = X_1 + X_2$, you effectively rotate and scale the axis, l | What is the intuition behind the independence of $X_2-X_1$ and $X_1+X_2$, $X_i \sim N(0,1)$?
This is standard normal distributed data:
Notice that the distribution is circulary symmetric.
When you switch to $Y_1 = X_2 - X_1$ and $Y_2 = X_1 + X_2$, you effectively rotate and scale the axis, like this:
This new coordin... | What is the intuition behind the independence of $X_2-X_1$ and $X_1+X_2$, $X_i \sim N(0,1)$?
This is standard normal distributed data:
Notice that the distribution is circulary symmetric.
When you switch to $Y_1 = X_2 - X_1$ and $Y_2 = X_1 + X_2$, you effectively rotate and scale the axis, l |
15,287 | What is the intuition behind the independence of $X_2-X_1$ and $X_1+X_2$, $X_i \sim N(0,1)$? | The result works for $(X_1,X_2)$ jointly normal (i.e. with correlation, $-1<\rho<1$), with common $\sigma$.
If you know a couple of basic results, this is about all you need:
$\quad\quad\quad$
dobiwan's approach is essentially fine - it's just that the result is more general than the case dealt with there. | What is the intuition behind the independence of $X_2-X_1$ and $X_1+X_2$, $X_i \sim N(0,1)$? | The result works for $(X_1,X_2)$ jointly normal (i.e. with correlation, $-1<\rho<1$), with common $\sigma$.
If you know a couple of basic results, this is about all you need:
$\quad\quad\quad$
dobiw | What is the intuition behind the independence of $X_2-X_1$ and $X_1+X_2$, $X_i \sim N(0,1)$?
The result works for $(X_1,X_2)$ jointly normal (i.e. with correlation, $-1<\rho<1$), with common $\sigma$.
If you know a couple of basic results, this is about all you need:
$\quad\quad\quad$
dobiwan's approach is essentiall... | What is the intuition behind the independence of $X_2-X_1$ and $X_1+X_2$, $X_i \sim N(0,1)$?
The result works for $(X_1,X_2)$ jointly normal (i.e. with correlation, $-1<\rho<1$), with common $\sigma$.
If you know a couple of basic results, this is about all you need:
$\quad\quad\quad$
dobiw |
15,288 | What is the intuition behind the independence of $X_2-X_1$ and $X_1+X_2$, $X_i \sim N(0,1)$? | The result you claim to be true is not true in general, not even for the case when all that is known is that $X_1$ and $X_2$ are normal random variables with identical variance, but the result does hold for the usual interpretation of the condition you stated later:
The subscripts do not indicate Order Statistics but ... | What is the intuition behind the independence of $X_2-X_1$ and $X_1+X_2$, $X_i \sim N(0,1)$? | The result you claim to be true is not true in general, not even for the case when all that is known is that $X_1$ and $X_2$ are normal random variables with identical variance, but the result does ho | What is the intuition behind the independence of $X_2-X_1$ and $X_1+X_2$, $X_i \sim N(0,1)$?
The result you claim to be true is not true in general, not even for the case when all that is known is that $X_1$ and $X_2$ are normal random variables with identical variance, but the result does hold for the usual interpreta... | What is the intuition behind the independence of $X_2-X_1$ and $X_1+X_2$, $X_i \sim N(0,1)$?
The result you claim to be true is not true in general, not even for the case when all that is known is that $X_1$ and $X_2$ are normal random variables with identical variance, but the result does ho |
15,289 | What is the intuition behind the independence of $X_2-X_1$ and $X_1+X_2$, $X_i \sim N(0,1)$? | I first argue for general identically distributed $X_1,X_2$ that the conditional mean of $Y_1$ conditional on $Y_2$ is constant $0$. Based on this, I argue that the covariance of $Y_1,Y_2$ is 0. Then, under normality, zero covariance implies independence.
The conditional mean
Intuition: $X_1+X_2=y$ does not imply anyt... | What is the intuition behind the independence of $X_2-X_1$ and $X_1+X_2$, $X_i \sim N(0,1)$? | I first argue for general identically distributed $X_1,X_2$ that the conditional mean of $Y_1$ conditional on $Y_2$ is constant $0$. Based on this, I argue that the covariance of $Y_1,Y_2$ is 0. Then | What is the intuition behind the independence of $X_2-X_1$ and $X_1+X_2$, $X_i \sim N(0,1)$?
I first argue for general identically distributed $X_1,X_2$ that the conditional mean of $Y_1$ conditional on $Y_2$ is constant $0$. Based on this, I argue that the covariance of $Y_1,Y_2$ is 0. Then, under normality, zero cov... | What is the intuition behind the independence of $X_2-X_1$ and $X_1+X_2$, $X_i \sim N(0,1)$?
I first argue for general identically distributed $X_1,X_2$ that the conditional mean of $Y_1$ conditional on $Y_2$ is constant $0$. Based on this, I argue that the covariance of $Y_1,Y_2$ is 0. Then |
15,290 | Testing randomly generated data against its intended distribution | Here is a general description of how the 3 methods mentioned work.
The Chi-Squared method works by comparing the number of observations in a bin to the number expected to be in the bin based on the distribution. For discrete distributions the bins are usually the discrete possibilities or combinations of those. For c... | Testing randomly generated data against its intended distribution | Here is a general description of how the 3 methods mentioned work.
The Chi-Squared method works by comparing the number of observations in a bin to the number expected to be in the bin based on the di | Testing randomly generated data against its intended distribution
Here is a general description of how the 3 methods mentioned work.
The Chi-Squared method works by comparing the number of observations in a bin to the number expected to be in the bin based on the distribution. For discrete distributions the bins are u... | Testing randomly generated data against its intended distribution
Here is a general description of how the 3 methods mentioned work.
The Chi-Squared method works by comparing the number of observations in a bin to the number expected to be in the bin based on the di |
15,291 | Testing randomly generated data against its intended distribution | +1 for writing a clear and detailed question. I hope that my answer isn't too frustrating. I believe that hypothesis testing is not an appropriate approach in your case. Null hypothesis significance testing is a reasonable thing to do when the answer could be yes or no, but you don't know which. (Unfortunately, it ... | Testing randomly generated data against its intended distribution | +1 for writing a clear and detailed question. I hope that my answer isn't too frustrating. I believe that hypothesis testing is not an appropriate approach in your case. Null hypothesis significanc | Testing randomly generated data against its intended distribution
+1 for writing a clear and detailed question. I hope that my answer isn't too frustrating. I believe that hypothesis testing is not an appropriate approach in your case. Null hypothesis significance testing is a reasonable thing to do when the answer ... | Testing randomly generated data against its intended distribution
+1 for writing a clear and detailed question. I hope that my answer isn't too frustrating. I believe that hypothesis testing is not an appropriate approach in your case. Null hypothesis significanc |
15,292 | Testing randomly generated data against its intended distribution | I haven't completely read all the answers but I do see they are pretty thorough and accurate. Running the risk that I am repeating something buried in the long answers I just want to say that v=the chi square test can be used for continuous data. It may not be the best test and like many tests it relies on asymptotic ... | Testing randomly generated data against its intended distribution | I haven't completely read all the answers but I do see they are pretty thorough and accurate. Running the risk that I am repeating something buried in the long answers I just want to say that v=the ch | Testing randomly generated data against its intended distribution
I haven't completely read all the answers but I do see they are pretty thorough and accurate. Running the risk that I am repeating something buried in the long answers I just want to say that v=the chi square test can be used for continuous data. It may... | Testing randomly generated data against its intended distribution
I haven't completely read all the answers but I do see they are pretty thorough and accurate. Running the risk that I am repeating something buried in the long answers I just want to say that v=the ch |
15,293 | Differences between prior distribution and prior predictive distribution? | Predictive here means predictive for observations. The prior distribution is a distribution for the parameters whereas the prior predictive distribution is a distribution for the observations.
If $X$ denotes the observations and we use the model (or likelihood) $p(x \mid \theta)$ for $\theta \in \Theta$ then a prior d... | Differences between prior distribution and prior predictive distribution? | Predictive here means predictive for observations. The prior distribution is a distribution for the parameters whereas the prior predictive distribution is a distribution for the observations.
If $X$ | Differences between prior distribution and prior predictive distribution?
Predictive here means predictive for observations. The prior distribution is a distribution for the parameters whereas the prior predictive distribution is a distribution for the observations.
If $X$ denotes the observations and we use the model ... | Differences between prior distribution and prior predictive distribution?
Predictive here means predictive for observations. The prior distribution is a distribution for the parameters whereas the prior predictive distribution is a distribution for the observations.
If $X$ |
15,294 | Differences between prior distribution and prior predictive distribution? | Let $Y$ be a random variable representing the (maybe future) data. We have a (parametric) model for $Y$ with $Y \sim f(y \mid \theta), \theta \in \Theta$, $\Theta$ the parameter space. Then we have a prior distribution represented by $\pi(\theta)$. Given an observation of $Y$, the posterior distribution of $\theta$ is ... | Differences between prior distribution and prior predictive distribution? | Let $Y$ be a random variable representing the (maybe future) data. We have a (parametric) model for $Y$ with $Y \sim f(y \mid \theta), \theta \in \Theta$, $\Theta$ the parameter space. Then we have a | Differences between prior distribution and prior predictive distribution?
Let $Y$ be a random variable representing the (maybe future) data. We have a (parametric) model for $Y$ with $Y \sim f(y \mid \theta), \theta \in \Theta$, $\Theta$ the parameter space. Then we have a prior distribution represented by $\pi(\theta)... | Differences between prior distribution and prior predictive distribution?
Let $Y$ be a random variable representing the (maybe future) data. We have a (parametric) model for $Y$ with $Y \sim f(y \mid \theta), \theta \in \Theta$, $\Theta$ the parameter space. Then we have a |
15,295 | Why is increasing the non-linearity of neural networks desired? | That part of the Wikipedia article leaves a bit to be desired. Let's separate two aspects:
The need for nonlinear activation functions
It's obvious that a feedforward neural network with linear activation functions and $n$ layers each having $m$ hidden units (linear neural network, for brevity) is equivalent to a linea... | Why is increasing the non-linearity of neural networks desired? | That part of the Wikipedia article leaves a bit to be desired. Let's separate two aspects:
The need for nonlinear activation functions
It's obvious that a feedforward neural network with linear activa | Why is increasing the non-linearity of neural networks desired?
That part of the Wikipedia article leaves a bit to be desired. Let's separate two aspects:
The need for nonlinear activation functions
It's obvious that a feedforward neural network with linear activation functions and $n$ layers each having $m$ hidden uni... | Why is increasing the non-linearity of neural networks desired?
That part of the Wikipedia article leaves a bit to be desired. Let's separate two aspects:
The need for nonlinear activation functions
It's obvious that a feedforward neural network with linear activa |
15,296 | Why is increasing the non-linearity of neural networks desired? | I'll give you a very loose analogy (emphasis is important here) that may help you understand the intuition. There's this technical drawing tool, called a French curve, here's an example:
We were trained to use it in high school in a technical drawing class. These days, the same class is taught with CAD software, so yo... | Why is increasing the non-linearity of neural networks desired? | I'll give you a very loose analogy (emphasis is important here) that may help you understand the intuition. There's this technical drawing tool, called a French curve, here's an example:
We were trai | Why is increasing the non-linearity of neural networks desired?
I'll give you a very loose analogy (emphasis is important here) that may help you understand the intuition. There's this technical drawing tool, called a French curve, here's an example:
We were trained to use it in high school in a technical drawing clas... | Why is increasing the non-linearity of neural networks desired?
I'll give you a very loose analogy (emphasis is important here) that may help you understand the intuition. There's this technical drawing tool, called a French curve, here's an example:
We were trai |
15,297 | Why is increasing the non-linearity of neural networks desired? | Why is increasing non-linearity desired?
Simply put: the more 'non-linear' our decision function, the more complex decisions it can make. In many cases this is desired because the decision function we are modeling with the neural network is unlikely to have a linear relationship with the input. Having more neurons in ... | Why is increasing the non-linearity of neural networks desired? | Why is increasing non-linearity desired?
Simply put: the more 'non-linear' our decision function, the more complex decisions it can make. In many cases this is desired because the decision function w | Why is increasing the non-linearity of neural networks desired?
Why is increasing non-linearity desired?
Simply put: the more 'non-linear' our decision function, the more complex decisions it can make. In many cases this is desired because the decision function we are modeling with the neural network is unlikely to ha... | Why is increasing the non-linearity of neural networks desired?
Why is increasing non-linearity desired?
Simply put: the more 'non-linear' our decision function, the more complex decisions it can make. In many cases this is desired because the decision function w |
15,298 | Why is increasing the non-linearity of neural networks desired? | Because linear model has limited "capacity" to perform the task. Consider the data set shown here Why does feature engineering work ?, we cannot draw a line to separate two classes.
On the other hand, using nonlinear transformation (feature engineering), the classification tasks becomes easy.
For neural network, it is ... | Why is increasing the non-linearity of neural networks desired? | Because linear model has limited "capacity" to perform the task. Consider the data set shown here Why does feature engineering work ?, we cannot draw a line to separate two classes.
On the other hand, | Why is increasing the non-linearity of neural networks desired?
Because linear model has limited "capacity" to perform the task. Consider the data set shown here Why does feature engineering work ?, we cannot draw a line to separate two classes.
On the other hand, using nonlinear transformation (feature engineering), t... | Why is increasing the non-linearity of neural networks desired?
Because linear model has limited "capacity" to perform the task. Consider the data set shown here Why does feature engineering work ?, we cannot draw a line to separate two classes.
On the other hand, |
15,299 | Why is increasing the non-linearity of neural networks desired? | it depends on your task. If you are doing processing of linear data (eg. text processing) you actually do not need non-linearity. But most of signal processing (image/audio) tasks are non-linear there you have to have non-linear layers. | Why is increasing the non-linearity of neural networks desired? | it depends on your task. If you are doing processing of linear data (eg. text processing) you actually do not need non-linearity. But most of signal processing (image/audio) tasks are non-linear there | Why is increasing the non-linearity of neural networks desired?
it depends on your task. If you are doing processing of linear data (eg. text processing) you actually do not need non-linearity. But most of signal processing (image/audio) tasks are non-linear there you have to have non-linear layers. | Why is increasing the non-linearity of neural networks desired?
it depends on your task. If you are doing processing of linear data (eg. text processing) you actually do not need non-linearity. But most of signal processing (image/audio) tasks are non-linear there |
15,300 | Why is increasing the non-linearity of neural networks desired? | That sounds like it was written by someone who doesn't know what they are talking about. Having a non-linearity is important because it allows the subsequent layers to build off each other. Two consecutive linear layers have the same power (they can represent the exact same set of functions) as a single linear layer. T... | Why is increasing the non-linearity of neural networks desired? | That sounds like it was written by someone who doesn't know what they are talking about. Having a non-linearity is important because it allows the subsequent layers to build off each other. Two consec | Why is increasing the non-linearity of neural networks desired?
That sounds like it was written by someone who doesn't know what they are talking about. Having a non-linearity is important because it allows the subsequent layers to build off each other. Two consecutive linear layers have the same power (they can repres... | Why is increasing the non-linearity of neural networks desired?
That sounds like it was written by someone who doesn't know what they are talking about. Having a non-linearity is important because it allows the subsequent layers to build off each other. Two consec |
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