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 |
|---|---|---|---|---|---|---|
50,901 | GLMM for overdispersed data | Overdispersion Problem
It looks like you're modeling a count variable as a binomial and I think that's the source of your overdispersion.
You could model everything as a binomial distribution, but the total for each observation is exactly the same. Plus, the count of diseased plants never reaches the maximum of 100, s... | GLMM for overdispersed data | Overdispersion Problem
It looks like you're modeling a count variable as a binomial and I think that's the source of your overdispersion.
You could model everything as a binomial distribution, but th | GLMM for overdispersed data
Overdispersion Problem
It looks like you're modeling a count variable as a binomial and I think that's the source of your overdispersion.
You could model everything as a binomial distribution, but the total for each observation is exactly the same. Plus, the count of diseased plants never r... | GLMM for overdispersed data
Overdispersion Problem
It looks like you're modeling a count variable as a binomial and I think that's the source of your overdispersion.
You could model everything as a binomial distribution, but th |
50,902 | GLMM for overdispersed data | you are correct that you have modeled it appropriately. Each of your flowers is "nested" under a tree and so are not independent of each other. Your code is appropriate where you have allowed the intercept to vary by trees.
It also looks like you have examined the intraclass correlation (i.e. the overdisp_fun() that y... | GLMM for overdispersed data | you are correct that you have modeled it appropriately. Each of your flowers is "nested" under a tree and so are not independent of each other. Your code is appropriate where you have allowed the inte | GLMM for overdispersed data
you are correct that you have modeled it appropriately. Each of your flowers is "nested" under a tree and so are not independent of each other. Your code is appropriate where you have allowed the intercept to vary by trees.
It also looks like you have examined the intraclass correlation (i.... | GLMM for overdispersed data
you are correct that you have modeled it appropriately. Each of your flowers is "nested" under a tree and so are not independent of each other. Your code is appropriate where you have allowed the inte |
50,903 | Word2Vec models for irrelevant word order | 1.
The simplest idea would be to copy the tags several times with different orderings, to make the data (sort of) invariant to permuting tags. In computer vision people do something similar to achieve translation invariance.
You could also use node2vec if you have a meaningful graph structure on your tags (for example ... | Word2Vec models for irrelevant word order | 1.
The simplest idea would be to copy the tags several times with different orderings, to make the data (sort of) invariant to permuting tags. In computer vision people do something similar to achieve | Word2Vec models for irrelevant word order
1.
The simplest idea would be to copy the tags several times with different orderings, to make the data (sort of) invariant to permuting tags. In computer vision people do something similar to achieve translation invariance.
You could also use node2vec if you have a meaningful ... | Word2Vec models for irrelevant word order
1.
The simplest idea would be to copy the tags several times with different orderings, to make the data (sort of) invariant to permuting tags. In computer vision people do something similar to achieve |
50,904 | Word2Vec models for irrelevant word order | I would suggest you study Word Embeddings a bit. You need to understand the mathematical significance of going from a one-hot encoded space to a dense vector space.
However to answer your questions (based on what I understood):
1) Assuming you are talking about English, use some pre-trained Word2Vec model and simply co... | Word2Vec models for irrelevant word order | I would suggest you study Word Embeddings a bit. You need to understand the mathematical significance of going from a one-hot encoded space to a dense vector space.
However to answer your questions (b | Word2Vec models for irrelevant word order
I would suggest you study Word Embeddings a bit. You need to understand the mathematical significance of going from a one-hot encoded space to a dense vector space.
However to answer your questions (based on what I understood):
1) Assuming you are talking about English, use som... | Word2Vec models for irrelevant word order
I would suggest you study Word Embeddings a bit. You need to understand the mathematical significance of going from a one-hot encoded space to a dense vector space.
However to answer your questions (b |
50,905 | Fisher Information Inequality of a function of a random variable | I am not aware of any general rule to pass over to the Fisher information of the floor function, but in this case it is possible to solve the problem for the specific distribution. The easiest way to do this is to explicitly determine the distribution of $Y = \text{floor} (X)$ and then find the Fisher information for ... | Fisher Information Inequality of a function of a random variable | I am not aware of any general rule to pass over to the Fisher information of the floor function, but in this case it is possible to solve the problem for the specific distribution. The easiest way to | Fisher Information Inequality of a function of a random variable
I am not aware of any general rule to pass over to the Fisher information of the floor function, but in this case it is possible to solve the problem for the specific distribution. The easiest way to do this is to explicitly determine the distribution of... | Fisher Information Inequality of a function of a random variable
I am not aware of any general rule to pass over to the Fisher information of the floor function, but in this case it is possible to solve the problem for the specific distribution. The easiest way to |
50,906 | Testing variable importance in prediction | I have used the randomForest package in R several times and there were some functions to measure the variable importance such as importance() and varImpPlot(). As far as I know varImpPlot visualizes the the importance of each predictor with respect to variables' contribution in the decrease of error measures (e.g mean ... | Testing variable importance in prediction | I have used the randomForest package in R several times and there were some functions to measure the variable importance such as importance() and varImpPlot(). As far as I know varImpPlot visualizes t | Testing variable importance in prediction
I have used the randomForest package in R several times and there were some functions to measure the variable importance such as importance() and varImpPlot(). As far as I know varImpPlot visualizes the the importance of each predictor with respect to variables' contribution in... | Testing variable importance in prediction
I have used the randomForest package in R several times and there were some functions to measure the variable importance such as importance() and varImpPlot(). As far as I know varImpPlot visualizes t |
50,907 | Measuring the bias-variance tradeoff | $EEPSE = EE(\hat{f}(x_0)-y_0)^2$ is the average (or "expected", over different predictions $\hat{f}(x_0)$ coming from different datasets) expected prediction squared error (for a particular $\hat{f}(x_0)$ over many test points $y_0$, at $x_0$)
To measure the bias-variance trade-off in a concrete example with concrete ... | Measuring the bias-variance tradeoff | $EEPSE = EE(\hat{f}(x_0)-y_0)^2$ is the average (or "expected", over different predictions $\hat{f}(x_0)$ coming from different datasets) expected prediction squared error (for a particular $\hat{f}( | Measuring the bias-variance tradeoff
$EEPSE = EE(\hat{f}(x_0)-y_0)^2$ is the average (or "expected", over different predictions $\hat{f}(x_0)$ coming from different datasets) expected prediction squared error (for a particular $\hat{f}(x_0)$ over many test points $y_0$, at $x_0$)
To measure the bias-variance trade-off... | Measuring the bias-variance tradeoff
$EEPSE = EE(\hat{f}(x_0)-y_0)^2$ is the average (or "expected", over different predictions $\hat{f}(x_0)$ coming from different datasets) expected prediction squared error (for a particular $\hat{f}( |
50,908 | Measuring the bias-variance tradeoff | $E(y_0-\hat{f}(x_0))^2 = Var(\hat{f}(x_0)) + Bias(\hat{f}(x_0))^2 + Var(\epsilon)$
Where
- $E(y_0-\hat{f}(x_0))^2$ is the expected test MSE.
- $Var(\hat{f}(x_0))$ is variance of fitted model.
- $Bias(\hat{f}(x_0))^2$ is squared biased of model.
- $Var(\epsilon)$ is variance of error terms
Source: James, G., Witten,... | Measuring the bias-variance tradeoff | $E(y_0-\hat{f}(x_0))^2 = Var(\hat{f}(x_0)) + Bias(\hat{f}(x_0))^2 + Var(\epsilon)$
Where
- $E(y_0-\hat{f}(x_0))^2$ is the expected test MSE.
- $Var(\hat{f}(x_0))$ is variance of fitted model.
- $Bi | Measuring the bias-variance tradeoff
$E(y_0-\hat{f}(x_0))^2 = Var(\hat{f}(x_0)) + Bias(\hat{f}(x_0))^2 + Var(\epsilon)$
Where
- $E(y_0-\hat{f}(x_0))^2$ is the expected test MSE.
- $Var(\hat{f}(x_0))$ is variance of fitted model.
- $Bias(\hat{f}(x_0))^2$ is squared biased of model.
- $Var(\epsilon)$ is variance of e... | Measuring the bias-variance tradeoff
$E(y_0-\hat{f}(x_0))^2 = Var(\hat{f}(x_0)) + Bias(\hat{f}(x_0))^2 + Var(\epsilon)$
Where
- $E(y_0-\hat{f}(x_0))^2$ is the expected test MSE.
- $Var(\hat{f}(x_0))$ is variance of fitted model.
- $Bi |
50,909 | How do I show that the sample median minimizes the sum of absolute deviations? [duplicate] | When all of the $x_i$ are distinct, this is easy. If $$f(m) = \sum_{i=1}^{n} |x_i - m|$$ then $$f'(m) = \sum_{i=1}^{n} {\rm sign}(x_i - m)$$ which equals zero when there are an equal number of elements of $x_1, ..., x_m$ that are above and below $m$, which is the definition of the median, $m^{\star}$. As a function of ... | How do I show that the sample median minimizes the sum of absolute deviations? [duplicate] | When all of the $x_i$ are distinct, this is easy. If $$f(m) = \sum_{i=1}^{n} |x_i - m|$$ then $$f'(m) = \sum_{i=1}^{n} {\rm sign}(x_i - m)$$ which equals zero when there are an equal number of element | How do I show that the sample median minimizes the sum of absolute deviations? [duplicate]
When all of the $x_i$ are distinct, this is easy. If $$f(m) = \sum_{i=1}^{n} |x_i - m|$$ then $$f'(m) = \sum_{i=1}^{n} {\rm sign}(x_i - m)$$ which equals zero when there are an equal number of elements of $x_1, ..., x_m$ that are... | How do I show that the sample median minimizes the sum of absolute deviations? [duplicate]
When all of the $x_i$ are distinct, this is easy. If $$f(m) = \sum_{i=1}^{n} |x_i - m|$$ then $$f'(m) = \sum_{i=1}^{n} {\rm sign}(x_i - m)$$ which equals zero when there are an equal number of element |
50,910 | Half-normal probability plot | There are many different "finite-sample/discrete sample" corrections, see for example here for the Normal probability plot (not the half normal).
Looking at Kutner et al. book, I see that they "link" their Half-normal probability plot formula to the following formula for the Normal PP:
$$\Phi^{-1}\left(\frac{k-0.375}... | Half-normal probability plot | There are many different "finite-sample/discrete sample" corrections, see for example here for the Normal probability plot (not the half normal).
Looking at Kutner et al. book, I see that they "link | Half-normal probability plot
There are many different "finite-sample/discrete sample" corrections, see for example here for the Normal probability plot (not the half normal).
Looking at Kutner et al. book, I see that they "link" their Half-normal probability plot formula to the following formula for the Normal PP:
$$... | Half-normal probability plot
There are many different "finite-sample/discrete sample" corrections, see for example here for the Normal probability plot (not the half normal).
Looking at Kutner et al. book, I see that they "link |
50,911 | The expected long run proportion of time the chain spends at $a$ , given that it starts at $c$ | What is the expected long run proportion of time the chain spends at $a$, given that it starts at $b$?
This exercise, technically, asks for the limiting probability value $\ell_b(a)$. You can note that the limiting distribution $\ell_b= \left(\frac{5}{13}, \frac{8}{13}, 0, 0, 0\right)$ that you correctly evaluated is ... | The expected long run proportion of time the chain spends at $a$ , given that it starts at $c$ | What is the expected long run proportion of time the chain spends at $a$, given that it starts at $b$?
This exercise, technically, asks for the limiting probability value $\ell_b(a)$. You can note th | The expected long run proportion of time the chain spends at $a$ , given that it starts at $c$
What is the expected long run proportion of time the chain spends at $a$, given that it starts at $b$?
This exercise, technically, asks for the limiting probability value $\ell_b(a)$. You can note that the limiting distribut... | The expected long run proportion of time the chain spends at $a$ , given that it starts at $c$
What is the expected long run proportion of time the chain spends at $a$, given that it starts at $b$?
This exercise, technically, asks for the limiting probability value $\ell_b(a)$. You can note th |
50,912 | Outliers in Linear Regression that ONLY revert significance | I am on the side of saying this is merely another way of understanding deletion diagnostics. Perturbations of a point are highly related to that point's influence function which is also estimated by deletion diagnostics. Typically the df-betas are used to show deletion diagnostics, but they can be scaled to be standard... | Outliers in Linear Regression that ONLY revert significance | I am on the side of saying this is merely another way of understanding deletion diagnostics. Perturbations of a point are highly related to that point's influence function which is also estimated by d | Outliers in Linear Regression that ONLY revert significance
I am on the side of saying this is merely another way of understanding deletion diagnostics. Perturbations of a point are highly related to that point's influence function which is also estimated by deletion diagnostics. Typically the df-betas are used to show... | Outliers in Linear Regression that ONLY revert significance
I am on the side of saying this is merely another way of understanding deletion diagnostics. Perturbations of a point are highly related to that point's influence function which is also estimated by d |
50,913 | How to analyse growth rate in R? | It would appear to me that you have daily time series data where you are measuring two characteristics. I would initially suggest using augmented ARIMA methods incorporating both memory and possible dummy indicators reflecting pulses. level/step shifts and/or time trends. Secondly it is possible to additionally relate ... | How to analyse growth rate in R? | It would appear to me that you have daily time series data where you are measuring two characteristics. I would initially suggest using augmented ARIMA methods incorporating both memory and possible d | How to analyse growth rate in R?
It would appear to me that you have daily time series data where you are measuring two characteristics. I would initially suggest using augmented ARIMA methods incorporating both memory and possible dummy indicators reflecting pulses. level/step shifts and/or time trends. Secondly it is... | How to analyse growth rate in R?
It would appear to me that you have daily time series data where you are measuring two characteristics. I would initially suggest using augmented ARIMA methods incorporating both memory and possible d |
50,914 | Tutorial for feature extraction on unsupervised learning | A nice reference is Dimensionality Reduction A Short Tutorial
by Ali Ghodsi. It covers PCA, Locally Linear Embedding, Multidimensional Scaling and Isomap.
Dan Ventura provides us with some nice worked examples of
Manifold Learning
- specifically, PCA, LLE and ISOMAP
Kilian Weinberger has a nice web page devoted to M... | Tutorial for feature extraction on unsupervised learning | A nice reference is Dimensionality Reduction A Short Tutorial
by Ali Ghodsi. It covers PCA, Locally Linear Embedding, Multidimensional Scaling and Isomap.
Dan Ventura provides us with some nice worke | Tutorial for feature extraction on unsupervised learning
A nice reference is Dimensionality Reduction A Short Tutorial
by Ali Ghodsi. It covers PCA, Locally Linear Embedding, Multidimensional Scaling and Isomap.
Dan Ventura provides us with some nice worked examples of
Manifold Learning
- specifically, PCA, LLE and ... | Tutorial for feature extraction on unsupervised learning
A nice reference is Dimensionality Reduction A Short Tutorial
by Ali Ghodsi. It covers PCA, Locally Linear Embedding, Multidimensional Scaling and Isomap.
Dan Ventura provides us with some nice worke |
50,915 | Tutorial for feature extraction on unsupervised learning | There is definitely a lot of blogs and explorations and tutorials out there, unfortunately I don't know any. IF you want explanation check Udacity georgia techs ML course, they have a section about PCA/ICA(maybe NMF)
You can stack them. Sometimes PCA is run to reduce dimensions so then ICA/NMF doesn't have to as much w... | Tutorial for feature extraction on unsupervised learning | There is definitely a lot of blogs and explorations and tutorials out there, unfortunately I don't know any. IF you want explanation check Udacity georgia techs ML course, they have a section about PC | Tutorial for feature extraction on unsupervised learning
There is definitely a lot of blogs and explorations and tutorials out there, unfortunately I don't know any. IF you want explanation check Udacity georgia techs ML course, they have a section about PCA/ICA(maybe NMF)
You can stack them. Sometimes PCA is run to re... | Tutorial for feature extraction on unsupervised learning
There is definitely a lot of blogs and explorations and tutorials out there, unfortunately I don't know any. IF you want explanation check Udacity georgia techs ML course, they have a section about PC |
50,916 | Tutorial for feature extraction on unsupervised learning | I personally think feature extraction on unsupervised learning is not well defined. If there is no ground truth label in the data, what's the goal of feature extraction, i.e., how do we know the derived feature is good or bad?
We can have finite ways to derive new features from data but will not know if new features ar... | Tutorial for feature extraction on unsupervised learning | I personally think feature extraction on unsupervised learning is not well defined. If there is no ground truth label in the data, what's the goal of feature extraction, i.e., how do we know the deriv | Tutorial for feature extraction on unsupervised learning
I personally think feature extraction on unsupervised learning is not well defined. If there is no ground truth label in the data, what's the goal of feature extraction, i.e., how do we know the derived feature is good or bad?
We can have finite ways to derive ne... | Tutorial for feature extraction on unsupervised learning
I personally think feature extraction on unsupervised learning is not well defined. If there is no ground truth label in the data, what's the goal of feature extraction, i.e., how do we know the deriv |
50,917 | Neural networks - how can I interpret what a hidden layer is doing to my data? | From my own understanding, it is notoriously difficult to infer useful things from the weights of a complex neural network.
However, with 10 digits, you could play around a bit.
Don't look just at the hidden-layer neuron with the strongest weight for 3, look at the top few.
Then look at those hidden-layer neuron's inp... | Neural networks - how can I interpret what a hidden layer is doing to my data? | From my own understanding, it is notoriously difficult to infer useful things from the weights of a complex neural network.
However, with 10 digits, you could play around a bit.
Don't look just at the | Neural networks - how can I interpret what a hidden layer is doing to my data?
From my own understanding, it is notoriously difficult to infer useful things from the weights of a complex neural network.
However, with 10 digits, you could play around a bit.
Don't look just at the hidden-layer neuron with the strongest w... | Neural networks - how can I interpret what a hidden layer is doing to my data?
From my own understanding, it is notoriously difficult to infer useful things from the weights of a complex neural network.
However, with 10 digits, you could play around a bit.
Don't look just at the |
50,918 | Comparing differences of AIC of different data sets | The AIC criterion scales with the overall size of the dataset, and this is true for differences in AIC values as well.
The criterion is based on the relationship
$$
-2 \, \mathrm{E}[\log \mathrm{Pr}_{\hat \theta}(Y)] \approx -\frac{2}{N} \, \mathrm{E}[\mathrm{loglik}] + \frac{2d}{N}
$$
where $d$ is the number of parame... | Comparing differences of AIC of different data sets | The AIC criterion scales with the overall size of the dataset, and this is true for differences in AIC values as well.
The criterion is based on the relationship
$$
-2 \, \mathrm{E}[\log \mathrm{Pr}_{ | Comparing differences of AIC of different data sets
The AIC criterion scales with the overall size of the dataset, and this is true for differences in AIC values as well.
The criterion is based on the relationship
$$
-2 \, \mathrm{E}[\log \mathrm{Pr}_{\hat \theta}(Y)] \approx -\frac{2}{N} \, \mathrm{E}[\mathrm{loglik}]... | Comparing differences of AIC of different data sets
The AIC criterion scales with the overall size of the dataset, and this is true for differences in AIC values as well.
The criterion is based on the relationship
$$
-2 \, \mathrm{E}[\log \mathrm{Pr}_{ |
50,919 | Can you simultaneously fit logistic and ordinal logistic regression models? | This seems analogous to mixture models. These are commonly used when dealing with zero-inflated data, where one component of the model deals with the probability of getting zero vs non-zero results, while a second component deals with continuous variation in the data (usually counts). The second component can also mode... | Can you simultaneously fit logistic and ordinal logistic regression models? | This seems analogous to mixture models. These are commonly used when dealing with zero-inflated data, where one component of the model deals with the probability of getting zero vs non-zero results, w | Can you simultaneously fit logistic and ordinal logistic regression models?
This seems analogous to mixture models. These are commonly used when dealing with zero-inflated data, where one component of the model deals with the probability of getting zero vs non-zero results, while a second component deals with continuou... | Can you simultaneously fit logistic and ordinal logistic regression models?
This seems analogous to mixture models. These are commonly used when dealing with zero-inflated data, where one component of the model deals with the probability of getting zero vs non-zero results, w |
50,920 | Resources for building explanatory regression models? | One major difference between prediction and explanation is that it's hard to interpret interactions of order higher than two, even if they may be important for a better prediction. In addition, you may wish to restrict the model pool to hierarchical models only (if A*B is included, then both main effects, A and B, shou... | Resources for building explanatory regression models? | One major difference between prediction and explanation is that it's hard to interpret interactions of order higher than two, even if they may be important for a better prediction. In addition, you ma | Resources for building explanatory regression models?
One major difference between prediction and explanation is that it's hard to interpret interactions of order higher than two, even if they may be important for a better prediction. In addition, you may wish to restrict the model pool to hierarchical models only (if ... | Resources for building explanatory regression models?
One major difference between prediction and explanation is that it's hard to interpret interactions of order higher than two, even if they may be important for a better prediction. In addition, you ma |
50,921 | Linear Regression and cost per employee vs total cost | "As my boss was interested in determining if economies of scale were present for the cost per end user data...
I guess this means whether cost per user goes down as number of users increases.
You start by assuming something like
$$TC_i = B_0U_i^{\beta_1}e^{v_i} = f(U_i)$$
which to a degree is validated as a postulated... | Linear Regression and cost per employee vs total cost | "As my boss was interested in determining if economies of scale were present for the cost per end user data...
I guess this means whether cost per user goes down as number of users increases.
You sta | Linear Regression and cost per employee vs total cost
"As my boss was interested in determining if economies of scale were present for the cost per end user data...
I guess this means whether cost per user goes down as number of users increases.
You start by assuming something like
$$TC_i = B_0U_i^{\beta_1}e^{v_i} = f... | Linear Regression and cost per employee vs total cost
"As my boss was interested in determining if economies of scale were present for the cost per end user data...
I guess this means whether cost per user goes down as number of users increases.
You sta |
50,922 | Handling of categorical variables: rpart vs tree | Partially answered in comments:
I don't know the full reason, but CART uses a trick to reduce the
number of splits considered. For regression, the levels of a
categorical predictor are replaced by mean of the outcome; for binary
responses, levels are replaced by the proportion of outcomes in class
1 (see Elements of S... | Handling of categorical variables: rpart vs tree | Partially answered in comments:
I don't know the full reason, but CART uses a trick to reduce the
number of splits considered. For regression, the levels of a
categorical predictor are replaced by me | Handling of categorical variables: rpart vs tree
Partially answered in comments:
I don't know the full reason, but CART uses a trick to reduce the
number of splits considered. For regression, the levels of a
categorical predictor are replaced by mean of the outcome; for binary
responses, levels are replaced by the pro... | Handling of categorical variables: rpart vs tree
Partially answered in comments:
I don't know the full reason, but CART uses a trick to reduce the
number of splits considered. For regression, the levels of a
categorical predictor are replaced by me |
50,923 | Which is the best graph to describe a survival analysis with a time-dependent covariate? | The patients at risk in a survival analysis must start time at risk at entry into the risk set. For a time-dependent covariate, time zero is the time when the at risk unit transfers from the initial to the new status. Thus time at risk in this new state begins at zero and everyone transferring is alive by definition an... | Which is the best graph to describe a survival analysis with a time-dependent covariate? | The patients at risk in a survival analysis must start time at risk at entry into the risk set. For a time-dependent covariate, time zero is the time when the at risk unit transfers from the initial t | Which is the best graph to describe a survival analysis with a time-dependent covariate?
The patients at risk in a survival analysis must start time at risk at entry into the risk set. For a time-dependent covariate, time zero is the time when the at risk unit transfers from the initial to the new status. Thus time at ... | Which is the best graph to describe a survival analysis with a time-dependent covariate?
The patients at risk in a survival analysis must start time at risk at entry into the risk set. For a time-dependent covariate, time zero is the time when the at risk unit transfers from the initial t |
50,924 | Are there any differences between Approximate Dynamic programming and Adaptive dynamic programming | They both refer to the same thing.
Reference:
F. -Y. Wang, H. Zhang and D. Liu, "Adaptive Dynamic Programming: An Introduction," in IEEE Computational Intelligence Magazine, vol. 4, no. 2, pp. 39-47, May 2009, doi: 10.1109/MCI.2009.932261. | Are there any differences between Approximate Dynamic programming and Adaptive dynamic programming | They both refer to the same thing.
Reference:
F. -Y. Wang, H. Zhang and D. Liu, "Adaptive Dynamic Programming: An Introduction," in IEEE Computational Intelligence Magazine, vol. 4, no. 2, pp. 39-47, | Are there any differences between Approximate Dynamic programming and Adaptive dynamic programming
They both refer to the same thing.
Reference:
F. -Y. Wang, H. Zhang and D. Liu, "Adaptive Dynamic Programming: An Introduction," in IEEE Computational Intelligence Magazine, vol. 4, no. 2, pp. 39-47, May 2009, doi: 10.110... | Are there any differences between Approximate Dynamic programming and Adaptive dynamic programming
They both refer to the same thing.
Reference:
F. -Y. Wang, H. Zhang and D. Liu, "Adaptive Dynamic Programming: An Introduction," in IEEE Computational Intelligence Magazine, vol. 4, no. 2, pp. 39-47, |
50,925 | Recursive neural network implementation in TensorFlow | Take a look at Tensorflow Fold:
https://github.com/tensorflow/fold
TensorFlow Fold is a library for creating TensorFlow models that consume structured data, where the structure of the computation graph depends on the structure of the input data. | Recursive neural network implementation in TensorFlow | Take a look at Tensorflow Fold:
https://github.com/tensorflow/fold
TensorFlow Fold is a library for creating TensorFlow models that consume structured data, where the structure of the computation gra | Recursive neural network implementation in TensorFlow
Take a look at Tensorflow Fold:
https://github.com/tensorflow/fold
TensorFlow Fold is a library for creating TensorFlow models that consume structured data, where the structure of the computation graph depends on the structure of the input data. | Recursive neural network implementation in TensorFlow
Take a look at Tensorflow Fold:
https://github.com/tensorflow/fold
TensorFlow Fold is a library for creating TensorFlow models that consume structured data, where the structure of the computation gra |
50,926 | Recursive neural network implementation in TensorFlow | These types of architectures are awkward in Tensorflow. I think someone did implement them though. http://www.kdnuggets.com/2016/06/recursive-neural-networks-tensorflow.html
You might prefer to use DyNet, which was designed to allow for dynamically changing architectures. https://github.com/clab/dynet | Recursive neural network implementation in TensorFlow | These types of architectures are awkward in Tensorflow. I think someone did implement them though. http://www.kdnuggets.com/2016/06/recursive-neural-networks-tensorflow.html
You might prefer to use Dy | Recursive neural network implementation in TensorFlow
These types of architectures are awkward in Tensorflow. I think someone did implement them though. http://www.kdnuggets.com/2016/06/recursive-neural-networks-tensorflow.html
You might prefer to use DyNet, which was designed to allow for dynamically changing architec... | Recursive neural network implementation in TensorFlow
These types of architectures are awkward in Tensorflow. I think someone did implement them though. http://www.kdnuggets.com/2016/06/recursive-neural-networks-tensorflow.html
You might prefer to use Dy |
50,927 | Recursive neural network implementation in TensorFlow | I hope this is what you need:
https://github.com/sapruash/RecursiveNN:
Tensorflow implementation of Recursive Neural Networks using LSTM units as described in "Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks" by Kai Sheng Tai, Richard Socher, and Christopher D. Manning. | Recursive neural network implementation in TensorFlow | I hope this is what you need:
https://github.com/sapruash/RecursiveNN:
Tensorflow implementation of Recursive Neural Networks using LSTM units as described in "Improved Semantic Representations From | Recursive neural network implementation in TensorFlow
I hope this is what you need:
https://github.com/sapruash/RecursiveNN:
Tensorflow implementation of Recursive Neural Networks using LSTM units as described in "Improved Semantic Representations From Tree-Structured Long Short-Term Memory Networks" by Kai Sheng Tai,... | Recursive neural network implementation in TensorFlow
I hope this is what you need:
https://github.com/sapruash/RecursiveNN:
Tensorflow implementation of Recursive Neural Networks using LSTM units as described in "Improved Semantic Representations From |
50,928 | Can any observation lie more than 3 SDs from the mean if there are 10 observations constrained between 0 and 1? | I believe you are correct. I think the general sketch of the proof comes from Cantelli's Lemma (which is related to Chebyshev's Inequality).
Note that in our specific case, we get
$$ P((X-E[X])/\sigma \geq k) \leq 1/(1+k^2)=1/10 \qquad\text{for k=3 in our case}$$
If we want a strict inequality for the difference, e.g. ... | Can any observation lie more than 3 SDs from the mean if there are 10 observations constrained betwe | I believe you are correct. I think the general sketch of the proof comes from Cantelli's Lemma (which is related to Chebyshev's Inequality).
Note that in our specific case, we get
$$ P((X-E[X])/\sigma | Can any observation lie more than 3 SDs from the mean if there are 10 observations constrained between 0 and 1?
I believe you are correct. I think the general sketch of the proof comes from Cantelli's Lemma (which is related to Chebyshev's Inequality).
Note that in our specific case, we get
$$ P((X-E[X])/\sigma \geq k)... | Can any observation lie more than 3 SDs from the mean if there are 10 observations constrained betwe
I believe you are correct. I think the general sketch of the proof comes from Cantelli's Lemma (which is related to Chebyshev's Inequality).
Note that in our specific case, we get
$$ P((X-E[X])/\sigma |
50,929 | Reducing number of data points in excel while keeping the curve shape | I would do the following:
The data very obviously follow a power law. Fit this non-linear model and find the highest N residuals. Re-estimate the model using a linear spline at each of the residuals. Output the predicted values and their inputs as a sequence of N points. This can be N=72 or any value you want (higher i... | Reducing number of data points in excel while keeping the curve shape | I would do the following:
The data very obviously follow a power law. Fit this non-linear model and find the highest N residuals. Re-estimate the model using a linear spline at each of the residuals. | Reducing number of data points in excel while keeping the curve shape
I would do the following:
The data very obviously follow a power law. Fit this non-linear model and find the highest N residuals. Re-estimate the model using a linear spline at each of the residuals. Output the predicted values and their inputs as a ... | Reducing number of data points in excel while keeping the curve shape
I would do the following:
The data very obviously follow a power law. Fit this non-linear model and find the highest N residuals. Re-estimate the model using a linear spline at each of the residuals. |
50,930 | Reducing number of data points in excel while keeping the curve shape | You should try fitting your data with the equation below:
data_y = data^a + b1*sin(c1*data) + b2*sin(c2*data) + b3*sin(c3*data) + b4*sin(c4*data)+d
This is a combination of a power law and several trigonometric functions. These should be able to capture the characteristics of your data very well, and will reduce your ... | Reducing number of data points in excel while keeping the curve shape | You should try fitting your data with the equation below:
data_y = data^a + b1*sin(c1*data) + b2*sin(c2*data) + b3*sin(c3*data) + b4*sin(c4*data)+d
This is a combination of a power law and several tr | Reducing number of data points in excel while keeping the curve shape
You should try fitting your data with the equation below:
data_y = data^a + b1*sin(c1*data) + b2*sin(c2*data) + b3*sin(c3*data) + b4*sin(c4*data)+d
This is a combination of a power law and several trigonometric functions. These should be able to cap... | Reducing number of data points in excel while keeping the curve shape
You should try fitting your data with the equation below:
data_y = data^a + b1*sin(c1*data) + b2*sin(c2*data) + b3*sin(c3*data) + b4*sin(c4*data)+d
This is a combination of a power law and several tr |
50,931 | Why iterative estimation of dispersion in negative binomial glm | It's not the same but it can be close. In this example, offset and exponent are less than $10^{-3}$ apart between the poisson and negative binomial estimate. The Poisson dispersion estimate is 1.08, while the negative binomial is 1.09
R code:
library(ggplot2)
library(data.table)
library(MASS)
data=data.table(x=1:100)... | Why iterative estimation of dispersion in negative binomial glm | It's not the same but it can be close. In this example, offset and exponent are less than $10^{-3}$ apart between the poisson and negative binomial estimate. The Poisson dispersion estimate is 1.08, w | Why iterative estimation of dispersion in negative binomial glm
It's not the same but it can be close. In this example, offset and exponent are less than $10^{-3}$ apart between the poisson and negative binomial estimate. The Poisson dispersion estimate is 1.08, while the negative binomial is 1.09
R code:
library(ggpl... | Why iterative estimation of dispersion in negative binomial glm
It's not the same but it can be close. In this example, offset and exponent are less than $10^{-3}$ apart between the poisson and negative binomial estimate. The Poisson dispersion estimate is 1.08, w |
50,932 | "Normalising" join probability of n events, by taking n-th root | To expand on my comment about your specific application, here's a practical example about deciding if normalization is appropriate. The example isn't about probabilities but it makes a point about normalization in general.
Consider a school which has two children, one who is 10 years old and is 4ft tall, the other is 1... | "Normalising" join probability of n events, by taking n-th root | To expand on my comment about your specific application, here's a practical example about deciding if normalization is appropriate. The example isn't about probabilities but it makes a point about nor | "Normalising" join probability of n events, by taking n-th root
To expand on my comment about your specific application, here's a practical example about deciding if normalization is appropriate. The example isn't about probabilities but it makes a point about normalization in general.
Consider a school which has two c... | "Normalising" join probability of n events, by taking n-th root
To expand on my comment about your specific application, here's a practical example about deciding if normalization is appropriate. The example isn't about probabilities but it makes a point about nor |
50,933 | Bootstrapped confidence interval for the difference in means for paired data | The first method is no resampling test of which I'm aware in the literature. It seems like your goal, by resampling $X$ and $Y$ independently, is to generate data under the null hypothesis. This approach is inefficient because you are ignoring pairing in the design.
The preferred resampling method for generating data ... | Bootstrapped confidence interval for the difference in means for paired data | The first method is no resampling test of which I'm aware in the literature. It seems like your goal, by resampling $X$ and $Y$ independently, is to generate data under the null hypothesis. This appro | Bootstrapped confidence interval for the difference in means for paired data
The first method is no resampling test of which I'm aware in the literature. It seems like your goal, by resampling $X$ and $Y$ independently, is to generate data under the null hypothesis. This approach is inefficient because you are ignoring... | Bootstrapped confidence interval for the difference in means for paired data
The first method is no resampling test of which I'm aware in the literature. It seems like your goal, by resampling $X$ and $Y$ independently, is to generate data under the null hypothesis. This appro |
50,934 | Is there a software that can draw a Bayesian model from JAGS? | It looks like Rasmus Bååth did exactly what you are looking for, by providing R scripts via his distribution_diagrams repository. | Is there a software that can draw a Bayesian model from JAGS? | It looks like Rasmus Bååth did exactly what you are looking for, by providing R scripts via his distribution_diagrams repository. | Is there a software that can draw a Bayesian model from JAGS?
It looks like Rasmus Bååth did exactly what you are looking for, by providing R scripts via his distribution_diagrams repository. | Is there a software that can draw a Bayesian model from JAGS?
It looks like Rasmus Bååth did exactly what you are looking for, by providing R scripts via his distribution_diagrams repository. |
50,935 | Is there a software that can draw a Bayesian model from JAGS? | Expanding on the answer above, here's a blog post about exactly this issue:
http://doingbayesiandataanalysis.blogspot.com/2013/10/diagrams-for-hierarchical-models-new.html
The post described two different methods for creating the diagrams. | Is there a software that can draw a Bayesian model from JAGS? | Expanding on the answer above, here's a blog post about exactly this issue:
http://doingbayesiandataanalysis.blogspot.com/2013/10/diagrams-for-hierarchical-models-new.html
The post described two diffe | Is there a software that can draw a Bayesian model from JAGS?
Expanding on the answer above, here's a blog post about exactly this issue:
http://doingbayesiandataanalysis.blogspot.com/2013/10/diagrams-for-hierarchical-models-new.html
The post described two different methods for creating the diagrams. | Is there a software that can draw a Bayesian model from JAGS?
Expanding on the answer above, here's a blog post about exactly this issue:
http://doingbayesiandataanalysis.blogspot.com/2013/10/diagrams-for-hierarchical-models-new.html
The post described two diffe |
50,936 | What is the difference between dead neuron and killing the gradient? | They both get zero or very small gradients so they can barely get trained.
The difference is, the activation values of “dead” ReLU neurons are almost always zero, whereas the activation values of saturated sigmoid neurons are close to 0 or 1. | What is the difference between dead neuron and killing the gradient? | They both get zero or very small gradients so they can barely get trained.
The difference is, the activation values of “dead” ReLU neurons are almost always zero, whereas the activation values of satu | What is the difference between dead neuron and killing the gradient?
They both get zero or very small gradients so they can barely get trained.
The difference is, the activation values of “dead” ReLU neurons are almost always zero, whereas the activation values of saturated sigmoid neurons are close to 0 or 1. | What is the difference between dead neuron and killing the gradient?
They both get zero or very small gradients so they can barely get trained.
The difference is, the activation values of “dead” ReLU neurons are almost always zero, whereas the activation values of satu |
50,937 | What is the difference between dead neuron and killing the gradient? | Both of them will have very small gradients, hence both act as a showstopper to learning.
Difference is that the likelihood of dead Relu neurons is much less as compared to saturated sigmoids
The gradient of a sigmoid is:
S′(a)=S(a)(1−S(a))
When we start learning useful features in the later layers, the activations S(a... | What is the difference between dead neuron and killing the gradient? | Both of them will have very small gradients, hence both act as a showstopper to learning.
Difference is that the likelihood of dead Relu neurons is much less as compared to saturated sigmoids
The grad | What is the difference between dead neuron and killing the gradient?
Both of them will have very small gradients, hence both act as a showstopper to learning.
Difference is that the likelihood of dead Relu neurons is much less as compared to saturated sigmoids
The gradient of a sigmoid is:
S′(a)=S(a)(1−S(a))
When we st... | What is the difference between dead neuron and killing the gradient?
Both of them will have very small gradients, hence both act as a showstopper to learning.
Difference is that the likelihood of dead Relu neurons is much less as compared to saturated sigmoids
The grad |
50,938 | Should one use the same overdispersion parameter when comparing Binomial models? | On page 90 of McCullagh & Nelder, they state that many covariate selection procedures, including AIC minimization and tests using the F statistic, are equivalent to minimizing $Q = D + \alpha q \phi$. Here $D$ is the deviance, $\alpha$ is a function of the number of data points, $q$ is the number of covariates, and $\p... | Should one use the same overdispersion parameter when comparing Binomial models? | On page 90 of McCullagh & Nelder, they state that many covariate selection procedures, including AIC minimization and tests using the F statistic, are equivalent to minimizing $Q = D + \alpha q \phi$. | Should one use the same overdispersion parameter when comparing Binomial models?
On page 90 of McCullagh & Nelder, they state that many covariate selection procedures, including AIC minimization and tests using the F statistic, are equivalent to minimizing $Q = D + \alpha q \phi$. Here $D$ is the deviance, $\alpha$ is ... | Should one use the same overdispersion parameter when comparing Binomial models?
On page 90 of McCullagh & Nelder, they state that many covariate selection procedures, including AIC minimization and tests using the F statistic, are equivalent to minimizing $Q = D + \alpha q \phi$. |
50,939 | Should one use the same overdispersion parameter when comparing Binomial models? | I suspect the reason for the recommendation is that, in the old days, first the model was fit, then the dispersion parameter was calculated, and then the likelihood was adjusted for overdispersion. Deriving suitable test statistics for a LRT with adjusted dispersion parameters seems difficult, so it may be that people ... | Should one use the same overdispersion parameter when comparing Binomial models? | I suspect the reason for the recommendation is that, in the old days, first the model was fit, then the dispersion parameter was calculated, and then the likelihood was adjusted for overdispersion. De | Should one use the same overdispersion parameter when comparing Binomial models?
I suspect the reason for the recommendation is that, in the old days, first the model was fit, then the dispersion parameter was calculated, and then the likelihood was adjusted for overdispersion. Deriving suitable test statistics for a L... | Should one use the same overdispersion parameter when comparing Binomial models?
I suspect the reason for the recommendation is that, in the old days, first the model was fit, then the dispersion parameter was calculated, and then the likelihood was adjusted for overdispersion. De |
50,940 | Online learning in LSTM | How can we do online learning in any of those models? Does it basically mean to set the batch_size to 1?
yes
Any ideas and thoughts about this? Is online learning really helpful?
see Tradeoff batch size vs. number of iterations to train a neural network | Online learning in LSTM | How can we do online learning in any of those models? Does it basically mean to set the batch_size to 1?
yes
Any ideas and thoughts about this? Is online learning really helpful?
see Tradeoff batch | Online learning in LSTM
How can we do online learning in any of those models? Does it basically mean to set the batch_size to 1?
yes
Any ideas and thoughts about this? Is online learning really helpful?
see Tradeoff batch size vs. number of iterations to train a neural network | Online learning in LSTM
How can we do online learning in any of those models? Does it basically mean to set the batch_size to 1?
yes
Any ideas and thoughts about this? Is online learning really helpful?
see Tradeoff batch |
50,941 | Is there a heuristic for determining the size of a fully connected layer at the end of a CNN? | Choosing a network architecture is a bit of a "black art".
They might have tried multiple different parameters and chose one that worked well (evaluating each using cross-validation). Also, you can inform your choice by what has been reported in the research literature to work well on similar tasks, and use that as a ... | Is there a heuristic for determining the size of a fully connected layer at the end of a CNN? | Choosing a network architecture is a bit of a "black art".
They might have tried multiple different parameters and chose one that worked well (evaluating each using cross-validation). Also, you can i | Is there a heuristic for determining the size of a fully connected layer at the end of a CNN?
Choosing a network architecture is a bit of a "black art".
They might have tried multiple different parameters and chose one that worked well (evaluating each using cross-validation). Also, you can inform your choice by what ... | Is there a heuristic for determining the size of a fully connected layer at the end of a CNN?
Choosing a network architecture is a bit of a "black art".
They might have tried multiple different parameters and chose one that worked well (evaluating each using cross-validation). Also, you can i |
50,942 | Estimating the mean of a random variable from greater than/less than answers | Here is my take on this question. I will assume that:
$X_i \sim \mathcal{N}(\mu, \sigma^2)$, and the $X_i$'s are independent
$\mu$ is unknown
$\sigma^2$ is known (I'll discuss this assumption later.)
Part 1: ML estimation given some data
First, consider the case where we are given some data, and we want to estimate $... | Estimating the mean of a random variable from greater than/less than answers | Here is my take on this question. I will assume that:
$X_i \sim \mathcal{N}(\mu, \sigma^2)$, and the $X_i$'s are independent
$\mu$ is unknown
$\sigma^2$ is known (I'll discuss this assumption later.) | Estimating the mean of a random variable from greater than/less than answers
Here is my take on this question. I will assume that:
$X_i \sim \mathcal{N}(\mu, \sigma^2)$, and the $X_i$'s are independent
$\mu$ is unknown
$\sigma^2$ is known (I'll discuss this assumption later.)
Part 1: ML estimation given some data
Fir... | Estimating the mean of a random variable from greater than/less than answers
Here is my take on this question. I will assume that:
$X_i \sim \mathcal{N}(\mu, \sigma^2)$, and the $X_i$'s are independent
$\mu$ is unknown
$\sigma^2$ is known (I'll discuss this assumption later.) |
50,943 | How to compute the AUROC for a single categorical variable | In short: yes, you could use a (simple) model(s) to compute the AUC (AUROC) for categorial features too.
When you compute the AUC for an ordinal feature, you use the feature itself like you would use a classification model output and apply the threshold to it (of which one class lies below and the other lies above). No... | How to compute the AUROC for a single categorical variable | In short: yes, you could use a (simple) model(s) to compute the AUC (AUROC) for categorial features too.
When you compute the AUC for an ordinal feature, you use the feature itself like you would use | How to compute the AUROC for a single categorical variable
In short: yes, you could use a (simple) model(s) to compute the AUC (AUROC) for categorial features too.
When you compute the AUC for an ordinal feature, you use the feature itself like you would use a classification model output and apply the threshold to it (... | How to compute the AUROC for a single categorical variable
In short: yes, you could use a (simple) model(s) to compute the AUC (AUROC) for categorial features too.
When you compute the AUC for an ordinal feature, you use the feature itself like you would use |
50,944 | General-to-specific subset selection ("Autometrics") performing well in macroeconomics | Frank Harrell does not rule out intelligent use of backward elimination. He includes as a possibility (page 97, RMS, 2nd edition):
Do limited backwards step-down variable selection if parsimony is more
important than accuracy.
This, however, is only to be done in the context of an already well-specified model. It i... | General-to-specific subset selection ("Autometrics") performing well in macroeconomics | Frank Harrell does not rule out intelligent use of backward elimination. He includes as a possibility (page 97, RMS, 2nd edition):
Do limited backwards step-down variable selection if parsimony is mo | General-to-specific subset selection ("Autometrics") performing well in macroeconomics
Frank Harrell does not rule out intelligent use of backward elimination. He includes as a possibility (page 97, RMS, 2nd edition):
Do limited backwards step-down variable selection if parsimony is more
important than accuracy.
Th... | General-to-specific subset selection ("Autometrics") performing well in macroeconomics
Frank Harrell does not rule out intelligent use of backward elimination. He includes as a possibility (page 97, RMS, 2nd edition):
Do limited backwards step-down variable selection if parsimony is mo |
50,945 | If you know the central moments of the data $X$, find a function $f$ for which $f(X)$ has arbitrary central moments | The function $f(x)$ should be monotonically increasing, such that its inverse $g(y)=f^{-1}(y)$ is defined. Then you can use the theorem about random variable transformations, which says that, when $\varphi(x)$ is the probability density of $X$, then $Y=f(X)$ has the probability density
$$\frac{d}{dy}\Phi(g(y))=\varphi(... | If you know the central moments of the data $X$, find a function $f$ for which $f(X)$ has arbitrary | The function $f(x)$ should be monotonically increasing, such that its inverse $g(y)=f^{-1}(y)$ is defined. Then you can use the theorem about random variable transformations, which says that, when $\v | If you know the central moments of the data $X$, find a function $f$ for which $f(X)$ has arbitrary central moments
The function $f(x)$ should be monotonically increasing, such that its inverse $g(y)=f^{-1}(y)$ is defined. Then you can use the theorem about random variable transformations, which says that, when $\varph... | If you know the central moments of the data $X$, find a function $f$ for which $f(X)$ has arbitrary
The function $f(x)$ should be monotonically increasing, such that its inverse $g(y)=f^{-1}(y)$ is defined. Then you can use the theorem about random variable transformations, which says that, when $\v |
50,946 | How to determine which variables to be used for cluster analysis | https://www.researchgate.net/profile/Federico_Marini/publication/230276990_Finding_relevant_clustering_directions_in_highdimensional_data_using_Particle_Swarm_Optimization/links/550c0b570cf20637993960f2.pdf
This paper describes how you can find optimal clustering directions using particle swarm optimization. This algor... | How to determine which variables to be used for cluster analysis | https://www.researchgate.net/profile/Federico_Marini/publication/230276990_Finding_relevant_clustering_directions_in_highdimensional_data_using_Particle_Swarm_Optimization/links/550c0b570cf20637993960 | How to determine which variables to be used for cluster analysis
https://www.researchgate.net/profile/Federico_Marini/publication/230276990_Finding_relevant_clustering_directions_in_highdimensional_data_using_Particle_Swarm_Optimization/links/550c0b570cf20637993960f2.pdf
This paper describes how you can find optimal cl... | How to determine which variables to be used for cluster analysis
https://www.researchgate.net/profile/Federico_Marini/publication/230276990_Finding_relevant_clustering_directions_in_highdimensional_data_using_Particle_Swarm_Optimization/links/550c0b570cf20637993960 |
50,947 | Linear model comparison - which does my data fit best? | So I've been working on understanding this question for a while over the last 20 hours or so. There has been lots of useful discussion, but no definitive answer. I've found a couple resources that might help others understand why I've chosen to go this route.
1) Soil Equilibria: What happens to acid rain? By Sharon Ant... | Linear model comparison - which does my data fit best? | So I've been working on understanding this question for a while over the last 20 hours or so. There has been lots of useful discussion, but no definitive answer. I've found a couple resources that mig | Linear model comparison - which does my data fit best?
So I've been working on understanding this question for a while over the last 20 hours or so. There has been lots of useful discussion, but no definitive answer. I've found a couple resources that might help others understand why I've chosen to go this route.
1) So... | Linear model comparison - which does my data fit best?
So I've been working on understanding this question for a while over the last 20 hours or so. There has been lots of useful discussion, but no definitive answer. I've found a couple resources that mig |
50,948 | Garson's algorithm for fully connected LSTMs | I'm actually doing a bit of work on this stuff at the minute. From what I've read in the literature, the connection weight method is actually better as it takes into account the magnitude and sign of the network. So maybe that would be a better starting point.
http://www.sciencedirect.com/science/article/pii/S03043800... | Garson's algorithm for fully connected LSTMs | I'm actually doing a bit of work on this stuff at the minute. From what I've read in the literature, the connection weight method is actually better as it takes into account the magnitude and sign of | Garson's algorithm for fully connected LSTMs
I'm actually doing a bit of work on this stuff at the minute. From what I've read in the literature, the connection weight method is actually better as it takes into account the magnitude and sign of the network. So maybe that would be a better starting point.
http://www.sc... | Garson's algorithm for fully connected LSTMs
I'm actually doing a bit of work on this stuff at the minute. From what I've read in the literature, the connection weight method is actually better as it takes into account the magnitude and sign of |
50,949 | Nested Anova vs Multilevel Linear Models | After having spent a few days in the library, I still feel far from understanding all implications of the two options. However, I have learned a few things and if anyone else is in the same boat, I would very much recommend the book "Multilevel Analysis" by Snijders and Bosker to guide your decision. It is by far the b... | Nested Anova vs Multilevel Linear Models | After having spent a few days in the library, I still feel far from understanding all implications of the two options. However, I have learned a few things and if anyone else is in the same boat, I wo | Nested Anova vs Multilevel Linear Models
After having spent a few days in the library, I still feel far from understanding all implications of the two options. However, I have learned a few things and if anyone else is in the same boat, I would very much recommend the book "Multilevel Analysis" by Snijders and Bosker t... | Nested Anova vs Multilevel Linear Models
After having spent a few days in the library, I still feel far from understanding all implications of the two options. However, I have learned a few things and if anyone else is in the same boat, I wo |
50,950 | ELO rating for non-pairing sport + serious math | If you're interested in use (more than in development), you should give a try to rankade, our ranking system.
Rankade is free and easy to use, and it's different from Elo ranking system (here's a comparison) because it can manage matches with 2+ players, and so it fits your needs, maybe.
Rankade's algorithm (ree algori... | ELO rating for non-pairing sport + serious math | If you're interested in use (more than in development), you should give a try to rankade, our ranking system.
Rankade is free and easy to use, and it's different from Elo ranking system (here's a comp | ELO rating for non-pairing sport + serious math
If you're interested in use (more than in development), you should give a try to rankade, our ranking system.
Rankade is free and easy to use, and it's different from Elo ranking system (here's a comparison) because it can manage matches with 2+ players, and so it fits yo... | ELO rating for non-pairing sport + serious math
If you're interested in use (more than in development), you should give a try to rankade, our ranking system.
Rankade is free and easy to use, and it's different from Elo ranking system (here's a comp |
50,951 | R - How to fix NbClust error with error message: "The TSS matrix is indefinite. There must be too many missing values." | The answer you linked seems to suggest that negative eigenvalues tend to crop up with larger values of max.nc. So maybe try reducing that to something reasonable? I don't know how you'd go about interpreting 993 clusters in any case. | R - How to fix NbClust error with error message: "The TSS matrix is indefinite. There must be too ma | The answer you linked seems to suggest that negative eigenvalues tend to crop up with larger values of max.nc. So maybe try reducing that to something reasonable? I don't know how you'd go about inter | R - How to fix NbClust error with error message: "The TSS matrix is indefinite. There must be too many missing values."
The answer you linked seems to suggest that negative eigenvalues tend to crop up with larger values of max.nc. So maybe try reducing that to something reasonable? I don't know how you'd go about inter... | R - How to fix NbClust error with error message: "The TSS matrix is indefinite. There must be too ma
The answer you linked seems to suggest that negative eigenvalues tend to crop up with larger values of max.nc. So maybe try reducing that to something reasonable? I don't know how you'd go about inter |
50,952 | Is the choice of test statistics in hypothesis testing a completely philosophical one? | I am assuming that you are asking about the choice of test statistic within a specific statistical model rather than asking about the choice of statistical model. I am also assuming that you are asking about the test statistic to be used in a classical hypothesis test in the accept/reject manner.
The choice of test sta... | Is the choice of test statistics in hypothesis testing a completely philosophical one? | I am assuming that you are asking about the choice of test statistic within a specific statistical model rather than asking about the choice of statistical model. I am also assuming that you are askin | Is the choice of test statistics in hypothesis testing a completely philosophical one?
I am assuming that you are asking about the choice of test statistic within a specific statistical model rather than asking about the choice of statistical model. I am also assuming that you are asking about the test statistic to be ... | Is the choice of test statistics in hypothesis testing a completely philosophical one?
I am assuming that you are asking about the choice of test statistic within a specific statistical model rather than asking about the choice of statistical model. I am also assuming that you are askin |
50,953 | How do I classify images with non-rectangle shape with CNN? | You can perfectly train non-rectangular images with CNN. if you have non rectangular input image, you can still use the square kernels. Note that the resulting size will be different in two dimensions. If you have 80*100 image, and use 5*5 filter, resulting dimension will be 76*96 if you do not use any padding. You hav... | How do I classify images with non-rectangle shape with CNN? | You can perfectly train non-rectangular images with CNN. if you have non rectangular input image, you can still use the square kernels. Note that the resulting size will be different in two dimensions | How do I classify images with non-rectangle shape with CNN?
You can perfectly train non-rectangular images with CNN. if you have non rectangular input image, you can still use the square kernels. Note that the resulting size will be different in two dimensions. If you have 80*100 image, and use 5*5 filter, resulting di... | How do I classify images with non-rectangle shape with CNN?
You can perfectly train non-rectangular images with CNN. if you have non rectangular input image, you can still use the square kernels. Note that the resulting size will be different in two dimensions |
50,954 | Link function in a Gamma-distribution GLM | why is the inverse used as the link function, i.e.: $μ=−(Xβ)^{−1}$
That's actually the mean-function $\mu(\eta)$. The link function is $\eta(\mu)$. However, both are in the form of a negative reciprocal in this case, since the negative of the reciprocal is its own inverse-function.
In particular, why is the inverse t... | Link function in a Gamma-distribution GLM | why is the inverse used as the link function, i.e.: $μ=−(Xβ)^{−1}$
That's actually the mean-function $\mu(\eta)$. The link function is $\eta(\mu)$. However, both are in the form of a negative recipro | Link function in a Gamma-distribution GLM
why is the inverse used as the link function, i.e.: $μ=−(Xβ)^{−1}$
That's actually the mean-function $\mu(\eta)$. The link function is $\eta(\mu)$. However, both are in the form of a negative reciprocal in this case, since the negative of the reciprocal is its own inverse-func... | Link function in a Gamma-distribution GLM
why is the inverse used as the link function, i.e.: $μ=−(Xβ)^{−1}$
That's actually the mean-function $\mu(\eta)$. The link function is $\eta(\mu)$. However, both are in the form of a negative recipro |
50,955 | Speed of convergence of probability | Consider $X_n$ such that $\mathbb P\left(X_n=n\right)=1/n$ and $\mathbb P\left(X_n=0\right)=1-1/n$, and $r_n:=1/n^2$. Since the sequence $X_n/r_n$ converges to $0$ in probability, we have in particular $X_n=O_p(r_n)$. However, for each positive $\varepsilon$, the quantity $\mathbb P\left(X_n\gt\varepsilon\right)$ is eq... | Speed of convergence of probability | Consider $X_n$ such that $\mathbb P\left(X_n=n\right)=1/n$ and $\mathbb P\left(X_n=0\right)=1-1/n$, and $r_n:=1/n^2$. Since the sequence $X_n/r_n$ converges to $0$ in probability, we have in particula | Speed of convergence of probability
Consider $X_n$ such that $\mathbb P\left(X_n=n\right)=1/n$ and $\mathbb P\left(X_n=0\right)=1-1/n$, and $r_n:=1/n^2$. Since the sequence $X_n/r_n$ converges to $0$ in probability, we have in particular $X_n=O_p(r_n)$. However, for each positive $\varepsilon$, the quantity $\mathbb P\... | Speed of convergence of probability
Consider $X_n$ such that $\mathbb P\left(X_n=n\right)=1/n$ and $\mathbb P\left(X_n=0\right)=1-1/n$, and $r_n:=1/n^2$. Since the sequence $X_n/r_n$ converges to $0$ in probability, we have in particula |
50,956 | Reject $H_0: \mu_1 = \mu_2 = \mu_3$ but not $H_0: \mu_1 = \mu_2, H_0: \mu_3 = \mu_2,$ or $H_0: \mu_1 = \mu_3$? [duplicate] | In mathematical terms, this amounts to check whether or not the region made of the intersection of
(x-y)²<8
(x-z)²<8
(z-y)²<8
(x-z)²+(x-y)²-(x-y)(x-z)>9
is empty. The first three constraints correspond to testing whether $X-Y\sim\mathcal{N}(0,2)$ is not too unlikely (and the same for $X-Z$ and $Y-Z$), while the last ... | Reject $H_0: \mu_1 = \mu_2 = \mu_3$ but not $H_0: \mu_1 = \mu_2, H_0: \mu_3 = \mu_2,$ or $H_0: \mu_1 | In mathematical terms, this amounts to check whether or not the region made of the intersection of
(x-y)²<8
(x-z)²<8
(z-y)²<8
(x-z)²+(x-y)²-(x-y)(x-z)>9
is empty. The first three constraints corresp | Reject $H_0: \mu_1 = \mu_2 = \mu_3$ but not $H_0: \mu_1 = \mu_2, H_0: \mu_3 = \mu_2,$ or $H_0: \mu_1 = \mu_3$? [duplicate]
In mathematical terms, this amounts to check whether or not the region made of the intersection of
(x-y)²<8
(x-z)²<8
(z-y)²<8
(x-z)²+(x-y)²-(x-y)(x-z)>9
is empty. The first three constraints corr... | Reject $H_0: \mu_1 = \mu_2 = \mu_3$ but not $H_0: \mu_1 = \mu_2, H_0: \mu_3 = \mu_2,$ or $H_0: \mu_1
In mathematical terms, this amounts to check whether or not the region made of the intersection of
(x-y)²<8
(x-z)²<8
(z-y)²<8
(x-z)²+(x-y)²-(x-y)(x-z)>9
is empty. The first three constraints corresp |
50,957 | Statistical significance, repeatability, and sample size (50 shades of grey) | You pose several important questions, some focusing on hypothesis testing, some on multiplicity, and so forth. These are my answers:
The typical approach is to repeat the experiment analyzing every time as no prior study has been conducted. So in a standard frequentist framework only this eventual p counts. I find it ... | Statistical significance, repeatability, and sample size (50 shades of grey) | You pose several important questions, some focusing on hypothesis testing, some on multiplicity, and so forth. These are my answers:
The typical approach is to repeat the experiment analyzing every t | Statistical significance, repeatability, and sample size (50 shades of grey)
You pose several important questions, some focusing on hypothesis testing, some on multiplicity, and so forth. These are my answers:
The typical approach is to repeat the experiment analyzing every time as no prior study has been conducted. S... | Statistical significance, repeatability, and sample size (50 shades of grey)
You pose several important questions, some focusing on hypothesis testing, some on multiplicity, and so forth. These are my answers:
The typical approach is to repeat the experiment analyzing every t |
50,958 | Time series with a sequence of zeros | A common approach to handle many zeroes in a time series is to use a Croston Model. To implement this model with your time series there are two R packages forecast and tsintermittent. The tsintermittent package optimizes the Croston model $\alpha$ parameter whereas the forecast package produces a forecast for a given ... | Time series with a sequence of zeros | A common approach to handle many zeroes in a time series is to use a Croston Model. To implement this model with your time series there are two R packages forecast and tsintermittent. The tsintermitt | Time series with a sequence of zeros
A common approach to handle many zeroes in a time series is to use a Croston Model. To implement this model with your time series there are two R packages forecast and tsintermittent. The tsintermittent package optimizes the Croston model $\alpha$ parameter whereas the forecast pac... | Time series with a sequence of zeros
A common approach to handle many zeroes in a time series is to use a Croston Model. To implement this model with your time series there are two R packages forecast and tsintermittent. The tsintermitt |
50,959 | Regression models with comparable MAE but differing R² | $R^2$ is a function of MSE loss.
$$
R^2=\dfrac{
\sum_i\big(
y_i-\hat y_i
\big)^2
}{\sum_i\big(
y_i-\bar y
\big)^2}\\=\dfrac{
nMSE
}{\sum_i\big(
y_i-\bar y
\big)^2}
$$
Consequently, if you are comparing two models on their respective $R^2$ values, you are implicitly using MSE. Any model with lower (worse) $R^2$ than ano... | Regression models with comparable MAE but differing R² | $R^2$ is a function of MSE loss.
$$
R^2=\dfrac{
\sum_i\big(
y_i-\hat y_i
\big)^2
}{\sum_i\big(
y_i-\bar y
\big)^2}\\=\dfrac{
nMSE
}{\sum_i\big(
y_i-\bar y
\big)^2}
$$
Consequently, if you are comparin | Regression models with comparable MAE but differing R²
$R^2$ is a function of MSE loss.
$$
R^2=\dfrac{
\sum_i\big(
y_i-\hat y_i
\big)^2
}{\sum_i\big(
y_i-\bar y
\big)^2}\\=\dfrac{
nMSE
}{\sum_i\big(
y_i-\bar y
\big)^2}
$$
Consequently, if you are comparing two models on their respective $R^2$ values, you are implicitly... | Regression models with comparable MAE but differing R²
$R^2$ is a function of MSE loss.
$$
R^2=\dfrac{
\sum_i\big(
y_i-\hat y_i
\big)^2
}{\sum_i\big(
y_i-\bar y
\big)^2}\\=\dfrac{
nMSE
}{\sum_i\big(
y_i-\bar y
\big)^2}
$$
Consequently, if you are comparin |
50,960 | What types of HMM are there? | I think your picture coming from Murphy's tutorial on graphical model.
And all these models can be named as "directed probabilistic graphical model" or "dynamic Bayesian network".
There are two problems we want to consider on these types of structured models
What is the structure (connectivity among random variables)... | What types of HMM are there? | I think your picture coming from Murphy's tutorial on graphical model.
And all these models can be named as "directed probabilistic graphical model" or "dynamic Bayesian network".
There are two probl | What types of HMM are there?
I think your picture coming from Murphy's tutorial on graphical model.
And all these models can be named as "directed probabilistic graphical model" or "dynamic Bayesian network".
There are two problems we want to consider on these types of structured models
What is the structure (connect... | What types of HMM are there?
I think your picture coming from Murphy's tutorial on graphical model.
And all these models can be named as "directed probabilistic graphical model" or "dynamic Bayesian network".
There are two probl |
50,961 | How to apply a model on dataset with missing data? | If you have access to the data set the model was trained on, you could impute new data and then compare means, standard deviations etc. to see how they differ.
You could also work backwards and use the model as is on a data set then compute statistics for that set, then try out different imputation techniques on the te... | How to apply a model on dataset with missing data? | If you have access to the data set the model was trained on, you could impute new data and then compare means, standard deviations etc. to see how they differ.
You could also work backwards and use th | How to apply a model on dataset with missing data?
If you have access to the data set the model was trained on, you could impute new data and then compare means, standard deviations etc. to see how they differ.
You could also work backwards and use the model as is on a data set then compute statistics for that set, the... | How to apply a model on dataset with missing data?
If you have access to the data set the model was trained on, you could impute new data and then compare means, standard deviations etc. to see how they differ.
You could also work backwards and use th |
50,962 | Minimal sufficiency with indicator functions | Someone described to me a clever proof sketch that I will flesh out for any interested parties.
First, let's fix the problem about having 0 in the denominator by constructing an equivalent condition:
$$
\frac{f(X|\theta)}{f(Y|\theta)} = \frac{ I( max(X) < \theta ) }{ I( max(Y) < \theta )} $$
is equivalent to:
$$ \exist... | Minimal sufficiency with indicator functions | Someone described to me a clever proof sketch that I will flesh out for any interested parties.
First, let's fix the problem about having 0 in the denominator by constructing an equivalent condition:
| Minimal sufficiency with indicator functions
Someone described to me a clever proof sketch that I will flesh out for any interested parties.
First, let's fix the problem about having 0 in the denominator by constructing an equivalent condition:
$$
\frac{f(X|\theta)}{f(Y|\theta)} = \frac{ I( max(X) < \theta ) }{ I( max(... | Minimal sufficiency with indicator functions
Someone described to me a clever proof sketch that I will flesh out for any interested parties.
First, let's fix the problem about having 0 in the denominator by constructing an equivalent condition:
|
50,963 | How does the complexity parameter correspond to the number of splits in cross validation in rpart? | There is one tree created, which is definitely overfitting the data. The specified minsplit essentially creates a tree that categorizes each terminal node into either all "present" or all "absent". rpart will not prune the tree for you, but can provide cross-validation for you to select the best subtree (i.e. select ... | How does the complexity parameter correspond to the number of splits in cross validation in rpart? | There is one tree created, which is definitely overfitting the data. The specified minsplit essentially creates a tree that categorizes each terminal node into either all "present" or all "absent". | How does the complexity parameter correspond to the number of splits in cross validation in rpart?
There is one tree created, which is definitely overfitting the data. The specified minsplit essentially creates a tree that categorizes each terminal node into either all "present" or all "absent". rpart will not prune ... | How does the complexity parameter correspond to the number of splits in cross validation in rpart?
There is one tree created, which is definitely overfitting the data. The specified minsplit essentially creates a tree that categorizes each terminal node into either all "present" or all "absent". |
50,964 | How to assess similarity of two sets of Principal Component Analysis loadings | Maybe the (modified) RV-coefficient is suitable for your problem. This measure computes the similarity/correlation between two matrices. Also note that a factor rotation is somewhat arbitrary, perhaps you should first do a procrustean factor rotation to 'match' the two factor structures. | How to assess similarity of two sets of Principal Component Analysis loadings | Maybe the (modified) RV-coefficient is suitable for your problem. This measure computes the similarity/correlation between two matrices. Also note that a factor rotation is somewhat arbitrary, perhaps | How to assess similarity of two sets of Principal Component Analysis loadings
Maybe the (modified) RV-coefficient is suitable for your problem. This measure computes the similarity/correlation between two matrices. Also note that a factor rotation is somewhat arbitrary, perhaps you should first do a procrustean factor ... | How to assess similarity of two sets of Principal Component Analysis loadings
Maybe the (modified) RV-coefficient is suitable for your problem. This measure computes the similarity/correlation between two matrices. Also note that a factor rotation is somewhat arbitrary, perhaps |
50,965 | Why does Box-Cox transformation fail in following situation? | (mostly copied from the comment by Nick Cox) The Box-Cox transform does not really fail: it is more that it is unnecessary, as there will be no need of transformation if max/min is small. Mostly, with max/min small all the observations are away from zero (relatively), so the power transform will be well approximated... | Why does Box-Cox transformation fail in following situation? | (mostly copied from the comment by Nick Cox) The Box-Cox transform does not really fail: it is more that it is unnecessary, as there will be no need of transformation if max/min is small. Mostly, w | Why does Box-Cox transformation fail in following situation?
(mostly copied from the comment by Nick Cox) The Box-Cox transform does not really fail: it is more that it is unnecessary, as there will be no need of transformation if max/min is small. Mostly, with max/min small all the observations are away from zero (... | Why does Box-Cox transformation fail in following situation?
(mostly copied from the comment by Nick Cox) The Box-Cox transform does not really fail: it is more that it is unnecessary, as there will be no need of transformation if max/min is small. Mostly, w |
50,966 | How to find the deterministic function representation of a random variable? | This can be accomplished using inverse transformation sampling; it does not require a restriction on the desired distribution function. To do this, define $X \equiv f(\theta,\omega) \equiv \inf \{ r \in \mathbb{R} | F_\theta(r) \geqslant \omega \}$$^\dagger$ where $F_\theta$ is the desired distribution function condit... | How to find the deterministic function representation of a random variable? | This can be accomplished using inverse transformation sampling; it does not require a restriction on the desired distribution function. To do this, define $X \equiv f(\theta,\omega) \equiv \inf \{ r | How to find the deterministic function representation of a random variable?
This can be accomplished using inverse transformation sampling; it does not require a restriction on the desired distribution function. To do this, define $X \equiv f(\theta,\omega) \equiv \inf \{ r \in \mathbb{R} | F_\theta(r) \geqslant \omeg... | How to find the deterministic function representation of a random variable?
This can be accomplished using inverse transformation sampling; it does not require a restriction on the desired distribution function. To do this, define $X \equiv f(\theta,\omega) \equiv \inf \{ r |
50,967 | n-gram language model | Language models are often used to compute the probability of a sentence. This is done by using the chain rule.
For example if we want to estimate the probability of observing the sentence $w_1 w_2 w_3 w_4$ we can factorize it like so...
$P(w_1, w_2, w_3, w_4) = P(w_4|w_3, w_2, w_1) P(w_3|w_2, w_1) P(w_2| w_1) P(w_1) $
... | n-gram language model | Language models are often used to compute the probability of a sentence. This is done by using the chain rule.
For example if we want to estimate the probability of observing the sentence $w_1 w_2 w_3 | n-gram language model
Language models are often used to compute the probability of a sentence. This is done by using the chain rule.
For example if we want to estimate the probability of observing the sentence $w_1 w_2 w_3 w_4$ we can factorize it like so...
$P(w_1, w_2, w_3, w_4) = P(w_4|w_3, w_2, w_1) P(w_3|w_2, w_1)... | n-gram language model
Language models are often used to compute the probability of a sentence. This is done by using the chain rule.
For example if we want to estimate the probability of observing the sentence $w_1 w_2 w_3 |
50,968 | How to specify when a level shift begins and ends or in the case of data series with multiple level shifts how to id when one level shift beings/ends? | I took a look at the data that you've posted. I appreciate your question because it gave me the opportunity to run it through with the software Autobox.
You are quite correct when you say that there is a structural break in 2004, but had you considered that there may be multiple candidates for a break in parameters? Se... | How to specify when a level shift begins and ends or in the case of data series with multiple level | I took a look at the data that you've posted. I appreciate your question because it gave me the opportunity to run it through with the software Autobox.
You are quite correct when you say that there i | How to specify when a level shift begins and ends or in the case of data series with multiple level shifts how to id when one level shift beings/ends?
I took a look at the data that you've posted. I appreciate your question because it gave me the opportunity to run it through with the software Autobox.
You are quite co... | How to specify when a level shift begins and ends or in the case of data series with multiple level
I took a look at the data that you've posted. I appreciate your question because it gave me the opportunity to run it through with the software Autobox.
You are quite correct when you say that there i |
50,969 | How to specify when a level shift begins and ends or in the case of data series with multiple level shifts how to id when one level shift beings/ends? | @whuber, It's been 5 years and our latest version does detect a break at obs 122 in 2009.
F TEST TO VERIFY CONSTANCY OF PARAMETERS
CANDIDATE BREAKPOINT F VALUE P VALUE
52 2003/ 4 3.53 .031490
... | How to specify when a level shift begins and ends or in the case of data series with multiple level | @whuber, It's been 5 years and our latest version does detect a break at obs 122 in 2009.
F TEST TO VERIFY CONSTANCY OF PARAMETERS
CANDIDATE BREAKPOINT F V | How to specify when a level shift begins and ends or in the case of data series with multiple level shifts how to id when one level shift beings/ends?
@whuber, It's been 5 years and our latest version does detect a break at obs 122 in 2009.
F TEST TO VERIFY CONSTANCY OF PARAMETERS
... | How to specify when a level shift begins and ends or in the case of data series with multiple level
@whuber, It's been 5 years and our latest version does detect a break at obs 122 in 2009.
F TEST TO VERIFY CONSTANCY OF PARAMETERS
CANDIDATE BREAKPOINT F V |
50,970 | What are the classification models that work on single-class classification problems? | There are plenty possibilities to construct one-class-classifiers. I wrote a number of simple algorithms in the context of authorship verification. Here, only positive samples of one author X are given, so that the task is to judge if a given document was written by X or not. However, it can be adapted to other fiel... | What are the classification models that work on single-class classification problems? | There are plenty possibilities to construct one-class-classifiers. I wrote a number of simple algorithms in the context of authorship verification. Here, only positive samples of one author X are gi | What are the classification models that work on single-class classification problems?
There are plenty possibilities to construct one-class-classifiers. I wrote a number of simple algorithms in the context of authorship verification. Here, only positive samples of one author X are given, so that the task is to judge ... | What are the classification models that work on single-class classification problems?
There are plenty possibilities to construct one-class-classifiers. I wrote a number of simple algorithms in the context of authorship verification. Here, only positive samples of one author X are gi |
50,971 | What are the classification models that work on single-class classification problems? | This is generally called One-Class Classification, Single-Class Classification, Outlier Detection or even Support Determination (i.e., what is the support of a distribution).
These generally attempt to solve the problem of low-density rejection (i.e., rejecting points that fall in areas where the training data has low ... | What are the classification models that work on single-class classification problems? | This is generally called One-Class Classification, Single-Class Classification, Outlier Detection or even Support Determination (i.e., what is the support of a distribution).
These generally attempt t | What are the classification models that work on single-class classification problems?
This is generally called One-Class Classification, Single-Class Classification, Outlier Detection or even Support Determination (i.e., what is the support of a distribution).
These generally attempt to solve the problem of low-density... | What are the classification models that work on single-class classification problems?
This is generally called One-Class Classification, Single-Class Classification, Outlier Detection or even Support Determination (i.e., what is the support of a distribution).
These generally attempt t |
50,972 | Permutation testing in multiply adjusted analyses | I think this paper might answer your question:
Permutation tests for univariate or multivariate by Marti J. Anderson
analysis of variance and regression http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.469.4226&rep=rep1&type=pdf
Think about two-way ANOVA and permute the rows. Then think about how to extend to ... | Permutation testing in multiply adjusted analyses | I think this paper might answer your question:
Permutation tests for univariate or multivariate by Marti J. Anderson
analysis of variance and regression http://citeseerx.ist.psu.edu/viewdoc/download? | Permutation testing in multiply adjusted analyses
I think this paper might answer your question:
Permutation tests for univariate or multivariate by Marti J. Anderson
analysis of variance and regression http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.469.4226&rep=rep1&type=pdf
Think about two-way ANOVA and pe... | Permutation testing in multiply adjusted analyses
I think this paper might answer your question:
Permutation tests for univariate or multivariate by Marti J. Anderson
analysis of variance and regression http://citeseerx.ist.psu.edu/viewdoc/download? |
50,973 | The meaning of Kernel density estimation | Normalized densities are essentially likelihood ratios, not true probability densities. They will no longer integrate to 1. Also, plotting the density values at 25%,50%,75% of the normalized max are not percentiles. To make this interpretable, you need to de-normalize and plot the level sets corresponding to some set o... | The meaning of Kernel density estimation | Normalized densities are essentially likelihood ratios, not true probability densities. They will no longer integrate to 1. Also, plotting the density values at 25%,50%,75% of the normalized max are n | The meaning of Kernel density estimation
Normalized densities are essentially likelihood ratios, not true probability densities. They will no longer integrate to 1. Also, plotting the density values at 25%,50%,75% of the normalized max are not percentiles. To make this interpretable, you need to de-normalize and plot t... | The meaning of Kernel density estimation
Normalized densities are essentially likelihood ratios, not true probability densities. They will no longer integrate to 1. Also, plotting the density values at 25%,50%,75% of the normalized max are n |
50,974 | Why is Levenberg-Marquardt only used with least squares problem? | Because it is based on a Second Order Approximation of the Squared Residual Function.
Hence it requires "Squared Residual Function".
The method will fit any model you can make which has the same form. | Why is Levenberg-Marquardt only used with least squares problem? | Because it is based on a Second Order Approximation of the Squared Residual Function.
Hence it requires "Squared Residual Function".
The method will fit any model you can make which has the same form. | Why is Levenberg-Marquardt only used with least squares problem?
Because it is based on a Second Order Approximation of the Squared Residual Function.
Hence it requires "Squared Residual Function".
The method will fit any model you can make which has the same form. | Why is Levenberg-Marquardt only used with least squares problem?
Because it is based on a Second Order Approximation of the Squared Residual Function.
Hence it requires "Squared Residual Function".
The method will fit any model you can make which has the same form. |
50,975 | Count Outcomes in Three Card Poker | Here's a Python program that I believe is correct. It takes a little under 7 minutes on my machine to run through every pair of hands.
from itertools import combinations
from collections import Counter
JACK, QUEEN, KING, ACE = 11, 12, 13, 14
N_HANDS = 22100 # 52 choose 3
deck = frozenset((rank, suit)
for ran... | Count Outcomes in Three Card Poker | Here's a Python program that I believe is correct. It takes a little under 7 minutes on my machine to run through every pair of hands.
from itertools import combinations
from collections import Counte | Count Outcomes in Three Card Poker
Here's a Python program that I believe is correct. It takes a little under 7 minutes on my machine to run through every pair of hands.
from itertools import combinations
from collections import Counter
JACK, QUEEN, KING, ACE = 11, 12, 13, 14
N_HANDS = 22100 # 52 choose 3
deck... | Count Outcomes in Three Card Poker
Here's a Python program that I believe is correct. It takes a little under 7 minutes on my machine to run through every pair of hands.
from itertools import combinations
from collections import Counte |
50,976 | How to build scoring model (scorecard) from logistic regression? | The basic ideas are not that difficult:
First model: You just multiply the respective coefficients with the new data points and see whether the sum is bigger than the negative intercept (then am is 1)
Second model: You first bin the numerical variables into distinct intervals (with cut()) and then run the logistic reg... | How to build scoring model (scorecard) from logistic regression? | The basic ideas are not that difficult:
First model: You just multiply the respective coefficients with the new data points and see whether the sum is bigger than the negative intercept (then am is 1 | How to build scoring model (scorecard) from logistic regression?
The basic ideas are not that difficult:
First model: You just multiply the respective coefficients with the new data points and see whether the sum is bigger than the negative intercept (then am is 1)
Second model: You first bin the numerical variables i... | How to build scoring model (scorecard) from logistic regression?
The basic ideas are not that difficult:
First model: You just multiply the respective coefficients with the new data points and see whether the sum is bigger than the negative intercept (then am is 1 |
50,977 | Use Available Pairs Method for Missing Data in OLS | It turns out there is a brief discussion of these methods in the book Missing Data Analysis by Little & Rubin chapter 3 section 4.
Matthai (1951) and Wilks (1932) discussed the available cases covariance estimator. They both suggest using the $n_{jk}-1$ degrees of freedom correction to covariance estimates where $n_{jk... | Use Available Pairs Method for Missing Data in OLS | It turns out there is a brief discussion of these methods in the book Missing Data Analysis by Little & Rubin chapter 3 section 4.
Matthai (1951) and Wilks (1932) discussed the available cases covaria | Use Available Pairs Method for Missing Data in OLS
It turns out there is a brief discussion of these methods in the book Missing Data Analysis by Little & Rubin chapter 3 section 4.
Matthai (1951) and Wilks (1932) discussed the available cases covariance estimator. They both suggest using the $n_{jk}-1$ degrees of free... | Use Available Pairs Method for Missing Data in OLS
It turns out there is a brief discussion of these methods in the book Missing Data Analysis by Little & Rubin chapter 3 section 4.
Matthai (1951) and Wilks (1932) discussed the available cases covaria |
50,978 | Alternatives to stepwise regression for generalized linear mixed models | How about the ensemble method of boostrapped aggregating, also known as bragging? Using this approach you essentially create a large number of replicates of the original dataset using simple random sampling with replacement (say 10,000 bootstrapped datasets) from your original dataset. Then you implement a variable s... | Alternatives to stepwise regression for generalized linear mixed models | How about the ensemble method of boostrapped aggregating, also known as bragging? Using this approach you essentially create a large number of replicates of the original dataset using simple random s | Alternatives to stepwise regression for generalized linear mixed models
How about the ensemble method of boostrapped aggregating, also known as bragging? Using this approach you essentially create a large number of replicates of the original dataset using simple random sampling with replacement (say 10,000 bootstrappe... | Alternatives to stepwise regression for generalized linear mixed models
How about the ensemble method of boostrapped aggregating, also known as bragging? Using this approach you essentially create a large number of replicates of the original dataset using simple random s |
50,979 | What is an example of data where the permutation test succeeds but a normal t-test fails? | In general, situations in which there is not enough data for the difference between the two sample means to have converged to something "near" the t distribution will cause the t-test to fail, in the sense of not having close to the specified probability of rejecting a true null hypothesis.
Let's assume we are drawing ... | What is an example of data where the permutation test succeeds but a normal t-test fails? | In general, situations in which there is not enough data for the difference between the two sample means to have converged to something "near" the t distribution will cause the t-test to fail, in the | What is an example of data where the permutation test succeeds but a normal t-test fails?
In general, situations in which there is not enough data for the difference between the two sample means to have converged to something "near" the t distribution will cause the t-test to fail, in the sense of not having close to t... | What is an example of data where the permutation test succeeds but a normal t-test fails?
In general, situations in which there is not enough data for the difference between the two sample means to have converged to something "near" the t distribution will cause the t-test to fail, in the |
50,980 | What is an example of data where the permutation test succeeds but a normal t-test fails? | I think a good example is the space shuttle data as given in the textbook $\it{The \ Statistical \ Sleuth}.$ The number of O-ring incidents is given by launch temperature as
Temp $ \ \ \ \ \ \ \ \ \ \ \ \ $ Number of O-Ring Incidents
$\lt 65^{\circ} \ \ \ \ \ \ \ \ \ \ \ \ \ \ 1,1,1,3$
$\gt 65^{\circ} \ \ \ \ \ \ \ \ ... | What is an example of data where the permutation test succeeds but a normal t-test fails? | I think a good example is the space shuttle data as given in the textbook $\it{The \ Statistical \ Sleuth}.$ The number of O-ring incidents is given by launch temperature as
Temp $ \ \ \ \ \ \ \ \ \ | What is an example of data where the permutation test succeeds but a normal t-test fails?
I think a good example is the space shuttle data as given in the textbook $\it{The \ Statistical \ Sleuth}.$ The number of O-ring incidents is given by launch temperature as
Temp $ \ \ \ \ \ \ \ \ \ \ \ \ $ Number of O-Ring Incid... | What is an example of data where the permutation test succeeds but a normal t-test fails?
I think a good example is the space shuttle data as given in the textbook $\it{The \ Statistical \ Sleuth}.$ The number of O-ring incidents is given by launch temperature as
Temp $ \ \ \ \ \ \ \ \ \ |
50,981 | Classification with unknown class | A viable alternative is to create two models:
High vs. Low & Other
Low vs. High & Other
You'll get probabilities $\text{P(High|Data)}$ and $\text{P(Low|Data)}$. If neither probability is higher than a threshold (say $50\%$) you can label the instance as $\text{Unknown}$ instead.
Example in R
An example in R using k... | Classification with unknown class | A viable alternative is to create two models:
High vs. Low & Other
Low vs. High & Other
You'll get probabilities $\text{P(High|Data)}$ and $\text{P(Low|Data)}$. If neither probability is higher tha | Classification with unknown class
A viable alternative is to create two models:
High vs. Low & Other
Low vs. High & Other
You'll get probabilities $\text{P(High|Data)}$ and $\text{P(Low|Data)}$. If neither probability is higher than a threshold (say $50\%$) you can label the instance as $\text{Unknown}$ instead.
Ex... | Classification with unknown class
A viable alternative is to create two models:
High vs. Low & Other
Low vs. High & Other
You'll get probabilities $\text{P(High|Data)}$ and $\text{P(Low|Data)}$. If neither probability is higher tha |
50,982 | Classification with unknown class | Yes, looks like anomaly detection problem. What you could also try is to generate artificial samples for your third class and train your model using them. Of course, the other question is how you generate it. But this highly depends on problem you solve. | Classification with unknown class | Yes, looks like anomaly detection problem. What you could also try is to generate artificial samples for your third class and train your model using them. Of course, the other question is how you gene | Classification with unknown class
Yes, looks like anomaly detection problem. What you could also try is to generate artificial samples for your third class and train your model using them. Of course, the other question is how you generate it. But this highly depends on problem you solve. | Classification with unknown class
Yes, looks like anomaly detection problem. What you could also try is to generate artificial samples for your third class and train your model using them. Of course, the other question is how you gene |
50,983 | Classification with unknown class | This depends on your classifier. Any method that assigns weights to each class and then use a decision rule could be modified to your method. For instance, a random forest typically uses majority voting. Say you have one with 1000 decision trees. You could modify the method so as to use majority vote only if one of you... | Classification with unknown class | This depends on your classifier. Any method that assigns weights to each class and then use a decision rule could be modified to your method. For instance, a random forest typically uses majority voti | Classification with unknown class
This depends on your classifier. Any method that assigns weights to each class and then use a decision rule could be modified to your method. For instance, a random forest typically uses majority voting. Say you have one with 1000 decision trees. You could modify the method so as to us... | Classification with unknown class
This depends on your classifier. Any method that assigns weights to each class and then use a decision rule could be modified to your method. For instance, a random forest typically uses majority voti |
50,984 | Classification with unknown class | Probably you don't need to change your classifier at all, just the way how you interpret results. E. g. if you have 2 classifiers and first one predict 10% probability that training sample belongs to it's class and the second one predict 9% probability, then it seems that training sample doesn't belong to any of that c... | Classification with unknown class | Probably you don't need to change your classifier at all, just the way how you interpret results. E. g. if you have 2 classifiers and first one predict 10% probability that training sample belongs to | Classification with unknown class
Probably you don't need to change your classifier at all, just the way how you interpret results. E. g. if you have 2 classifiers and first one predict 10% probability that training sample belongs to it's class and the second one predict 9% probability, then it seems that training samp... | Classification with unknown class
Probably you don't need to change your classifier at all, just the way how you interpret results. E. g. if you have 2 classifiers and first one predict 10% probability that training sample belongs to |
50,985 | Performing Cross Validation to Compare Lasso and Other Regression Models in R | I'm not entirely sure I understand precisely where in the analysis pipeline your question is, but I think I can address it by walking through the steps you'll want to take. The software portion of your question is off-topic on CV, but the questions about CV are on-topic, so I'll answer those.
My question is: is it tec... | Performing Cross Validation to Compare Lasso and Other Regression Models in R | I'm not entirely sure I understand precisely where in the analysis pipeline your question is, but I think I can address it by walking through the steps you'll want to take. The software portion of you | Performing Cross Validation to Compare Lasso and Other Regression Models in R
I'm not entirely sure I understand precisely where in the analysis pipeline your question is, but I think I can address it by walking through the steps you'll want to take. The software portion of your question is off-topic on CV, but the que... | Performing Cross Validation to Compare Lasso and Other Regression Models in R
I'm not entirely sure I understand precisely where in the analysis pipeline your question is, but I think I can address it by walking through the steps you'll want to take. The software portion of you |
50,986 | What is the difference between identifiable and estimable? | Identifiability is related to the mathematical model without consideration for any real-world noise in the observations.
Estimability takes measurement noise into account.
Jacquez, J.A. & P. Greif (1985) "Numerical Parameter Identifiability and Estimability: Integrating Identifiability, Estimability, and Optimal Sampli... | What is the difference between identifiable and estimable? | Identifiability is related to the mathematical model without consideration for any real-world noise in the observations.
Estimability takes measurement noise into account.
Jacquez, J.A. & P. Greif (19 | What is the difference between identifiable and estimable?
Identifiability is related to the mathematical model without consideration for any real-world noise in the observations.
Estimability takes measurement noise into account.
Jacquez, J.A. & P. Greif (1985) "Numerical Parameter Identifiability and Estimability: In... | What is the difference between identifiable and estimable?
Identifiability is related to the mathematical model without consideration for any real-world noise in the observations.
Estimability takes measurement noise into account.
Jacquez, J.A. & P. Greif (19 |
50,987 | Joint probability of a minimum and maximum score after $n$ dice rolls | I'll post an "answer," mainly composed of wikichung's ideas. We independently roll the unbiased die $n$ times obtaining $X_1, \dots, X_n$, with minimum value $m$ and maximum value $M$. What is $P(m=2, M=5)$? You noted that
$$
P(2 \leq X_i \leq 5; \forall i) = \prod_{i=1}^n \frac{4}{6} = \left(\frac{4}{6}\right)^n.
$... | Joint probability of a minimum and maximum score after $n$ dice rolls | I'll post an "answer," mainly composed of wikichung's ideas. We independently roll the unbiased die $n$ times obtaining $X_1, \dots, X_n$, with minimum value $m$ and maximum value $M$. What is $P(m= | Joint probability of a minimum and maximum score after $n$ dice rolls
I'll post an "answer," mainly composed of wikichung's ideas. We independently roll the unbiased die $n$ times obtaining $X_1, \dots, X_n$, with minimum value $m$ and maximum value $M$. What is $P(m=2, M=5)$? You noted that
$$
P(2 \leq X_i \leq 5; ... | Joint probability of a minimum and maximum score after $n$ dice rolls
I'll post an "answer," mainly composed of wikichung's ideas. We independently roll the unbiased die $n$ times obtaining $X_1, \dots, X_n$, with minimum value $m$ and maximum value $M$. What is $P(m= |
50,988 | Sales forecast with an ARIMA model | Since your data has an upward trend to it, it is good that your model has an upward trend. The data looks exponential, so using a log transform is a good idea.
However, it looks like your model's variance is lower than your data's variance. I would try more auto-regressive values. e.g. ARIMA(7,1,0), ARIMA(9,1,0), et... | Sales forecast with an ARIMA model | Since your data has an upward trend to it, it is good that your model has an upward trend. The data looks exponential, so using a log transform is a good idea.
However, it looks like your model's var | Sales forecast with an ARIMA model
Since your data has an upward trend to it, it is good that your model has an upward trend. The data looks exponential, so using a log transform is a good idea.
However, it looks like your model's variance is lower than your data's variance. I would try more auto-regressive values. ... | Sales forecast with an ARIMA model
Since your data has an upward trend to it, it is good that your model has an upward trend. The data looks exponential, so using a log transform is a good idea.
However, it looks like your model's var |
50,989 | Sales forecast with an ARIMA model | The forecast is upward trending which seems way too aggressive.
If you aren't looking or adjusting for outliers, your model isn't robust. For example, periods 263, 301, 319, 321, 322 and 339 don't follow the up down pattern.
A model with double differencing, an AR2 and a separate AR1 plus adjusting for the outliers m... | Sales forecast with an ARIMA model | The forecast is upward trending which seems way too aggressive.
If you aren't looking or adjusting for outliers, your model isn't robust. For example, periods 263, 301, 319, 321, 322 and 339 don't fol | Sales forecast with an ARIMA model
The forecast is upward trending which seems way too aggressive.
If you aren't looking or adjusting for outliers, your model isn't robust. For example, periods 263, 301, 319, 321, 322 and 339 don't follow the up down pattern.
A model with double differencing, an AR2 and a separate AR... | Sales forecast with an ARIMA model
The forecast is upward trending which seems way too aggressive.
If you aren't looking or adjusting for outliers, your model isn't robust. For example, periods 263, 301, 319, 321, 322 and 339 don't fol |
50,990 | Walkthrough of building a time series model (on real examples) | In terms of practical down-to-earth examples , I might suggest reviewing some of my 583 replies to time-series model building questions. It is the only subject that I know and feel competent to comment on and thus is the only area that I do so. @gung nicely pointed to one of them in his response. Most are real data ca... | Walkthrough of building a time series model (on real examples) | In terms of practical down-to-earth examples , I might suggest reviewing some of my 583 replies to time-series model building questions. It is the only subject that I know and feel competent to comme | Walkthrough of building a time series model (on real examples)
In terms of practical down-to-earth examples , I might suggest reviewing some of my 583 replies to time-series model building questions. It is the only subject that I know and feel competent to comment on and thus is the only area that I do so. @gung nicel... | Walkthrough of building a time series model (on real examples)
In terms of practical down-to-earth examples , I might suggest reviewing some of my 583 replies to time-series model building questions. It is the only subject that I know and feel competent to comme |
50,991 | Walkthrough of building a time series model (on real examples) | I'd expect most tutorials to start and end with descriptive time series analysis in an ARIMA framework, but it might also be interesting to look at tutorial treatments of structural time series analysis in a Bayesian framework with a focus on causal inference.
If that's your bag, my current go-to resources are the f... | Walkthrough of building a time series model (on real examples) | I'd expect most tutorials to start and end with descriptive time series analysis in an ARIMA framework, but it might also be interesting to look at tutorial treatments of structural time series analy | Walkthrough of building a time series model (on real examples)
I'd expect most tutorials to start and end with descriptive time series analysis in an ARIMA framework, but it might also be interesting to look at tutorial treatments of structural time series analysis in a Bayesian framework with a focus on causal infere... | Walkthrough of building a time series model (on real examples)
I'd expect most tutorials to start and end with descriptive time series analysis in an ARIMA framework, but it might also be interesting to look at tutorial treatments of structural time series analy |
50,992 | Walkthrough of building a time series model (on real examples) | MATLAB has a ton of end to end examples. Here's where one batch starts, just follow it to the end. It's part I, the last one is part X, but there are other related examples on multiplicative seasonality, de-trending, filters etc. | Walkthrough of building a time series model (on real examples) | MATLAB has a ton of end to end examples. Here's where one batch starts, just follow it to the end. It's part I, the last one is part X, but there are other related examples on multiplicative seasonali | Walkthrough of building a time series model (on real examples)
MATLAB has a ton of end to end examples. Here's where one batch starts, just follow it to the end. It's part I, the last one is part X, but there are other related examples on multiplicative seasonality, de-trending, filters etc. | Walkthrough of building a time series model (on real examples)
MATLAB has a ton of end to end examples. Here's where one batch starts, just follow it to the end. It's part I, the last one is part X, but there are other related examples on multiplicative seasonali |
50,993 | Is there something clever I can do with a log of a sum? | If you're analyzing the sum of geometric means then, I'm afraid you can't go too far. You may try applying geometric mean inequality to get an upper bounds:
$$ \left(a_1 a_2 \cdots a_n\right)^{1/n} \le \frac{1}{n} \sum_{k=1}^n a_k$$
Particularly, Poyla's proof looks promising.
Your starting expression is:
$$\left(\prod... | Is there something clever I can do with a log of a sum? | If you're analyzing the sum of geometric means then, I'm afraid you can't go too far. You may try applying geometric mean inequality to get an upper bounds:
$$ \left(a_1 a_2 \cdots a_n\right)^{1/n} \l | Is there something clever I can do with a log of a sum?
If you're analyzing the sum of geometric means then, I'm afraid you can't go too far. You may try applying geometric mean inequality to get an upper bounds:
$$ \left(a_1 a_2 \cdots a_n\right)^{1/n} \le \frac{1}{n} \sum_{k=1}^n a_k$$
Particularly, Poyla's proof loo... | Is there something clever I can do with a log of a sum?
If you're analyzing the sum of geometric means then, I'm afraid you can't go too far. You may try applying geometric mean inequality to get an upper bounds:
$$ \left(a_1 a_2 \cdots a_n\right)^{1/n} \l |
50,994 | What are the votes in R's unsupervised random Forest? | Reading from here we confirm your understanding of unsupervised Breiman's Random Forests.
Unsupervised learning
In unsupervised learning the data consist of a set of x -vectors of
the same dimension with no class labels or response variables. There
is no figure of merit to optimize, leaving the field open to ambig... | What are the votes in R's unsupervised random Forest? | Reading from here we confirm your understanding of unsupervised Breiman's Random Forests.
Unsupervised learning
In unsupervised learning the data consist of a set of x -vectors of
the same dimensio | What are the votes in R's unsupervised random Forest?
Reading from here we confirm your understanding of unsupervised Breiman's Random Forests.
Unsupervised learning
In unsupervised learning the data consist of a set of x -vectors of
the same dimension with no class labels or response variables. There
is no figure... | What are the votes in R's unsupervised random Forest?
Reading from here we confirm your understanding of unsupervised Breiman's Random Forests.
Unsupervised learning
In unsupervised learning the data consist of a set of x -vectors of
the same dimensio |
50,995 | What's the maximum expectation of a conditional variance, $E[\operatorname{Var}(X+Z_1 \mid X+Z_2)]$? | Motivated by Prof. Guo's paper & Prof. Kim's course materials (see links in the comments below), I've found the answer to this question. I'll post it below in case other folks run into similar questions.
The maximum value of $E[Var(X+Z_1\mid X+Z_2)]$ is $3/2$ and is indeed achieved by $X\sim N(0,1)$, as speculated in ... | What's the maximum expectation of a conditional variance, $E[\operatorname{Var}(X+Z_1 \mid X+Z_2)]$? | Motivated by Prof. Guo's paper & Prof. Kim's course materials (see links in the comments below), I've found the answer to this question. I'll post it below in case other folks run into similar questi | What's the maximum expectation of a conditional variance, $E[\operatorname{Var}(X+Z_1 \mid X+Z_2)]$?
Motivated by Prof. Guo's paper & Prof. Kim's course materials (see links in the comments below), I've found the answer to this question. I'll post it below in case other folks run into similar questions.
The maximum va... | What's the maximum expectation of a conditional variance, $E[\operatorname{Var}(X+Z_1 \mid X+Z_2)]$?
Motivated by Prof. Guo's paper & Prof. Kim's course materials (see links in the comments below), I've found the answer to this question. I'll post it below in case other folks run into similar questi |
50,996 | Shrinkage of the Sample Covariance matrix | What they evaluate there is
$$ \mathbb{E}\big[ (\alpha f + (1-\alpha)s - \sigma)^2 \big], $$
(note this isn't identical to what you wrote in the question) where you are given
$$ \mathbb{E}[f] = \phi, \qquad \mathbb{E}[s] = \sigma. $$
They evaluate this by writing
$$ \alpha f + (1-\alpha)s-\sigma =: X, $$
and using the ... | Shrinkage of the Sample Covariance matrix | What they evaluate there is
$$ \mathbb{E}\big[ (\alpha f + (1-\alpha)s - \sigma)^2 \big], $$
(note this isn't identical to what you wrote in the question) where you are given
$$ \mathbb{E}[f] = \phi, | Shrinkage of the Sample Covariance matrix
What they evaluate there is
$$ \mathbb{E}\big[ (\alpha f + (1-\alpha)s - \sigma)^2 \big], $$
(note this isn't identical to what you wrote in the question) where you are given
$$ \mathbb{E}[f] = \phi, \qquad \mathbb{E}[s] = \sigma. $$
They evaluate this by writing
$$ \alpha f + ... | Shrinkage of the Sample Covariance matrix
What they evaluate there is
$$ \mathbb{E}\big[ (\alpha f + (1-\alpha)s - \sigma)^2 \big], $$
(note this isn't identical to what you wrote in the question) where you are given
$$ \mathbb{E}[f] = \phi, |
50,997 | Modelling clustered data using boosted regression trees | If you want to use boosted trees instead of linear models, you essentially have three options: (i) you can ignore the grouping structure, (ii) you can include the grouping structure (i.e. the animal ID in your case) as a categorical variable (this corresponds to using fixed effects), (iii) or you can use a boosting alg... | Modelling clustered data using boosted regression trees | If you want to use boosted trees instead of linear models, you essentially have three options: (i) you can ignore the grouping structure, (ii) you can include the grouping structure (i.e. the animal I | Modelling clustered data using boosted regression trees
If you want to use boosted trees instead of linear models, you essentially have three options: (i) you can ignore the grouping structure, (ii) you can include the grouping structure (i.e. the animal ID in your case) as a categorical variable (this corresponds to u... | Modelling clustered data using boosted regression trees
If you want to use boosted trees instead of linear models, you essentially have three options: (i) you can ignore the grouping structure, (ii) you can include the grouping structure (i.e. the animal I |
50,998 | SVM with non-negative weights | Initially, I think the model is fine but I don't know any package can solve this model directly.
One method is that you may can try to write the dual form of this problem. I believe it will be a QP problem with extra nonnegative constrains.
Alternatively, you also can try to find some algorithms which updates $w_{i}$... | SVM with non-negative weights | Initially, I think the model is fine but I don't know any package can solve this model directly.
One method is that you may can try to write the dual form of this problem. I believe it will be a QP p | SVM with non-negative weights
Initially, I think the model is fine but I don't know any package can solve this model directly.
One method is that you may can try to write the dual form of this problem. I believe it will be a QP problem with extra nonnegative constrains.
Alternatively, you also can try to find some al... | SVM with non-negative weights
Initially, I think the model is fine but I don't know any package can solve this model directly.
One method is that you may can try to write the dual form of this problem. I believe it will be a QP p |
50,999 | SVM with non-negative weights | As @Ben Dai suggests, this looks like it will be a quadratic programming problem with inequality constraints. There are various general quadratic programming packages available which may be used to solve the problem, such as the quadprog routine in the MATLAB optimisation toolbox. This is only such routine I have use... | SVM with non-negative weights | As @Ben Dai suggests, this looks like it will be a quadratic programming problem with inequality constraints. There are various general quadratic programming packages available which may be used to s | SVM with non-negative weights
As @Ben Dai suggests, this looks like it will be a quadratic programming problem with inequality constraints. There are various general quadratic programming packages available which may be used to solve the problem, such as the quadprog routine in the MATLAB optimisation toolbox. This i... | SVM with non-negative weights
As @Ben Dai suggests, this looks like it will be a quadratic programming problem with inequality constraints. There are various general quadratic programming packages available which may be used to s |
51,000 | Confidence interval for the odds ratio in a finite population | In general, there is no problem with forming a confidence intervals when your sample is near the size of the (finite) population. You typically just need to use the finite population correction:
\begin{align}
{\rm finite}\ SE &= SE \times \sqrt{\frac{N-n}{N-1}} \\
\\
&= SE \times\sqrt{\frac{60-50}{... | Confidence interval for the odds ratio in a finite population | In general, there is no problem with forming a confidence intervals when your sample is near the size of the (finite) population. You typically just need to use the finite population correction:
\begi | Confidence interval for the odds ratio in a finite population
In general, there is no problem with forming a confidence intervals when your sample is near the size of the (finite) population. You typically just need to use the finite population correction:
\begin{align}
{\rm finite}\ SE &= SE \times \sqrt{\frac{N-n}{N-... | Confidence interval for the odds ratio in a finite population
In general, there is no problem with forming a confidence intervals when your sample is near the size of the (finite) population. You typically just need to use the finite population correction:
\begi |
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