idx int64 1 56k | question stringlengths 15 155 | answer stringlengths 2 29.2k ⌀ | question_cut stringlengths 15 100 | answer_cut stringlengths 2 200 ⌀ | conversation stringlengths 47 29.3k | conversation_cut stringlengths 47 301 |
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4,501 | What are the main theorems in Machine (Deep) Learning? | I wouldn't call it a main theorem, but I think the following (sometimes referred to as the Universal approximation theorem) is an interesting (and at least for me surprising) one as it states the approximative power of feed-forward neural networks.
Theorem:
Let $\sigma$ be a nonconstant and monotinically-increasing co... | What are the main theorems in Machine (Deep) Learning? | I wouldn't call it a main theorem, but I think the following (sometimes referred to as the Universal approximation theorem) is an interesting (and at least for me surprising) one as it states the appr | What are the main theorems in Machine (Deep) Learning?
I wouldn't call it a main theorem, but I think the following (sometimes referred to as the Universal approximation theorem) is an interesting (and at least for me surprising) one as it states the approximative power of feed-forward neural networks.
Theorem:
Let $\... | What are the main theorems in Machine (Deep) Learning?
I wouldn't call it a main theorem, but I think the following (sometimes referred to as the Universal approximation theorem) is an interesting (and at least for me surprising) one as it states the appr |
4,502 | What are the main theorems in Machine (Deep) Learning? | A nice post focusing on this question (specifically deep learning rather than general machine learning theorems) is here:
https://medium.com/mlreview/modern-theory-of-deep-learning-why-does-it-works-so-well-9ee1f7fb2808
It gives an accessible summary of the main emerging theorems for the ability of deep neural networks... | What are the main theorems in Machine (Deep) Learning? | A nice post focusing on this question (specifically deep learning rather than general machine learning theorems) is here:
https://medium.com/mlreview/modern-theory-of-deep-learning-why-does-it-works-s | What are the main theorems in Machine (Deep) Learning?
A nice post focusing on this question (specifically deep learning rather than general machine learning theorems) is here:
https://medium.com/mlreview/modern-theory-of-deep-learning-why-does-it-works-so-well-9ee1f7fb2808
It gives an accessible summary of the main em... | What are the main theorems in Machine (Deep) Learning?
A nice post focusing on this question (specifically deep learning rather than general machine learning theorems) is here:
https://medium.com/mlreview/modern-theory-of-deep-learning-why-does-it-works-s |
4,503 | Do we have to tune the number of trees in a random forest? | It's common to find code snippets that treat $T$ as a hyper-parameter, and attempt to optimize over it in the same way as any other hyper-parameter. This is just wasting computational power: when all other hyper-parameters are fixed, the model’s loss stochastically decreases as the number of trees increases.
Intuitive ... | Do we have to tune the number of trees in a random forest? | It's common to find code snippets that treat $T$ as a hyper-parameter, and attempt to optimize over it in the same way as any other hyper-parameter. This is just wasting computational power: when all | Do we have to tune the number of trees in a random forest?
It's common to find code snippets that treat $T$ as a hyper-parameter, and attempt to optimize over it in the same way as any other hyper-parameter. This is just wasting computational power: when all other hyper-parameters are fixed, the model’s loss stochastic... | Do we have to tune the number of trees in a random forest?
It's common to find code snippets that treat $T$ as a hyper-parameter, and attempt to optimize over it in the same way as any other hyper-parameter. This is just wasting computational power: when all |
4,504 | R libraries for deep learning | OpenSource h2o.deepLearning() is package for deeplearning in R from h2o.ai
here's a write up http://www.r-bloggers.com/things-to-try-after-user-part-1-deep-learning-with-h2o/
And code: https://gist.github.com/woobe/3e728e02f6cc03ab86d8#file-link_data-r
######## *Convert Breast Cancer data into H2O*
dat <- BreastCancer[... | R libraries for deep learning | OpenSource h2o.deepLearning() is package for deeplearning in R from h2o.ai
here's a write up http://www.r-bloggers.com/things-to-try-after-user-part-1-deep-learning-with-h2o/
And code: https://gist.gi | R libraries for deep learning
OpenSource h2o.deepLearning() is package for deeplearning in R from h2o.ai
here's a write up http://www.r-bloggers.com/things-to-try-after-user-part-1-deep-learning-with-h2o/
And code: https://gist.github.com/woobe/3e728e02f6cc03ab86d8#file-link_data-r
######## *Convert Breast Cancer data ... | R libraries for deep learning
OpenSource h2o.deepLearning() is package for deeplearning in R from h2o.ai
here's a write up http://www.r-bloggers.com/things-to-try-after-user-part-1-deep-learning-with-h2o/
And code: https://gist.gi |
4,505 | R libraries for deep learning | There is a package called "darch"
http://cran.um.ac.ir/web/packages/darch/index.html
Quote from CRAN:
darch: Package for deep architectures and Restricted-Bolzmann-Machines
The darch package is build on the basis of the code from G. E. Hinton
and R. R. Salakhutdinov (available under Matlab Code for deep belief
net... | R libraries for deep learning | There is a package called "darch"
http://cran.um.ac.ir/web/packages/darch/index.html
Quote from CRAN:
darch: Package for deep architectures and Restricted-Bolzmann-Machines
The darch package is build | R libraries for deep learning
There is a package called "darch"
http://cran.um.ac.ir/web/packages/darch/index.html
Quote from CRAN:
darch: Package for deep architectures and Restricted-Bolzmann-Machines
The darch package is build on the basis of the code from G. E. Hinton
and R. R. Salakhutdinov (available under Mat... | R libraries for deep learning
There is a package called "darch"
http://cran.um.ac.ir/web/packages/darch/index.html
Quote from CRAN:
darch: Package for deep architectures and Restricted-Bolzmann-Machines
The darch package is build |
4,506 | R libraries for deep learning | There's another new package for deep networks in R: deepnet
I haven't tried to use it yet, but it's already been incorporated into the caret package. | R libraries for deep learning | There's another new package for deep networks in R: deepnet
I haven't tried to use it yet, but it's already been incorporated into the caret package. | R libraries for deep learning
There's another new package for deep networks in R: deepnet
I haven't tried to use it yet, but it's already been incorporated into the caret package. | R libraries for deep learning
There's another new package for deep networks in R: deepnet
I haven't tried to use it yet, but it's already been incorporated into the caret package. |
4,507 | R libraries for deep learning | To answer my own question, I wrote a small package in R for RBMs: https://github.com/zachmayer/rbm
This package is still under heavy development, and I know very little about RBMs, so I'd welcome any feedback (and pull requests!) you have. You can install the package using devtools:
devtools:::install_github('zachmaye... | R libraries for deep learning | To answer my own question, I wrote a small package in R for RBMs: https://github.com/zachmayer/rbm
This package is still under heavy development, and I know very little about RBMs, so I'd welcome any | R libraries for deep learning
To answer my own question, I wrote a small package in R for RBMs: https://github.com/zachmayer/rbm
This package is still under heavy development, and I know very little about RBMs, so I'd welcome any feedback (and pull requests!) you have. You can install the package using devtools:
devto... | R libraries for deep learning
To answer my own question, I wrote a small package in R for RBMs: https://github.com/zachmayer/rbm
This package is still under heavy development, and I know very little about RBMs, so I'd welcome any |
4,508 | R libraries for deep learning | You can try H2O's Deep Learning module, it is distributed and offers many advanced techniques such as dropout regularization and adaptive learning rate.
Slides: http://www.slideshare.net/0xdata/h2o-deeplearning-nextml
Video: https://www.youtube.com/watch?v=gAKbAQu900w&feature=youtu.be
Tutorials: http://learn.h2o.ai
Dat... | R libraries for deep learning | You can try H2O's Deep Learning module, it is distributed and offers many advanced techniques such as dropout regularization and adaptive learning rate.
Slides: http://www.slideshare.net/0xdata/h2o-de | R libraries for deep learning
You can try H2O's Deep Learning module, it is distributed and offers many advanced techniques such as dropout regularization and adaptive learning rate.
Slides: http://www.slideshare.net/0xdata/h2o-deeplearning-nextml
Video: https://www.youtube.com/watch?v=gAKbAQu900w&feature=youtu.be
Tuto... | R libraries for deep learning
You can try H2O's Deep Learning module, it is distributed and offers many advanced techniques such as dropout regularization and adaptive learning rate.
Slides: http://www.slideshare.net/0xdata/h2o-de |
4,509 | R libraries for deep learning | To add another answer:
mxnet is amazing, and I love it It's a little difficult to install, but it supports GPUs and multiple CPUs. If you're going to do deep learning in R (particularly on images), I highly recommend you start with mxnet. | R libraries for deep learning | To add another answer:
mxnet is amazing, and I love it It's a little difficult to install, but it supports GPUs and multiple CPUs. If you're going to do deep learning in R (particularly on images), | R libraries for deep learning
To add another answer:
mxnet is amazing, and I love it It's a little difficult to install, but it supports GPUs and multiple CPUs. If you're going to do deep learning in R (particularly on images), I highly recommend you start with mxnet. | R libraries for deep learning
To add another answer:
mxnet is amazing, and I love it It's a little difficult to install, but it supports GPUs and multiple CPUs. If you're going to do deep learning in R (particularly on images), |
4,510 | R libraries for deep learning | While I haven't encountered a dedicated deep learning library for R, I have run into a similar discussion out on r-bloggers. The discussion centers on using RBM (Restricted Boltzman Machines). Take a look at the following link--
http://www.r-bloggers.com/restricted-boltzmann-machines-in-r/ (reposted from 'alandgraf.b... | R libraries for deep learning | While I haven't encountered a dedicated deep learning library for R, I have run into a similar discussion out on r-bloggers. The discussion centers on using RBM (Restricted Boltzman Machines). Take | R libraries for deep learning
While I haven't encountered a dedicated deep learning library for R, I have run into a similar discussion out on r-bloggers. The discussion centers on using RBM (Restricted Boltzman Machines). Take a look at the following link--
http://www.r-bloggers.com/restricted-boltzmann-machines-in-... | R libraries for deep learning
While I haven't encountered a dedicated deep learning library for R, I have run into a similar discussion out on r-bloggers. The discussion centers on using RBM (Restricted Boltzman Machines). Take |
4,511 | R libraries for deep learning | You can now also use TensorFlow from R:
https://rstudio.github.io/tensorflow/ | R libraries for deep learning | You can now also use TensorFlow from R:
https://rstudio.github.io/tensorflow/ | R libraries for deep learning
You can now also use TensorFlow from R:
https://rstudio.github.io/tensorflow/ | R libraries for deep learning
You can now also use TensorFlow from R:
https://rstudio.github.io/tensorflow/ |
4,512 | Intuition behind why Stein's paradox only applies in dimensions $\ge 3$ | The dichotomy between the cases $d < 3$ and $d \geq 3$ for the admissibility of the MLE of the mean of a $d$-dimensional multivariate normal random variable is certainly shocking.
There is another very famous example in probability and statistics in which there is a dichotomy between the $d < 3$ and $d \geq 3$ cases. T... | Intuition behind why Stein's paradox only applies in dimensions $\ge 3$ | The dichotomy between the cases $d < 3$ and $d \geq 3$ for the admissibility of the MLE of the mean of a $d$-dimensional multivariate normal random variable is certainly shocking.
There is another ver | Intuition behind why Stein's paradox only applies in dimensions $\ge 3$
The dichotomy between the cases $d < 3$ and $d \geq 3$ for the admissibility of the MLE of the mean of a $d$-dimensional multivariate normal random variable is certainly shocking.
There is another very famous example in probability and statistics i... | Intuition behind why Stein's paradox only applies in dimensions $\ge 3$
The dichotomy between the cases $d < 3$ and $d \geq 3$ for the admissibility of the MLE of the mean of a $d$-dimensional multivariate normal random variable is certainly shocking.
There is another ver |
4,513 | Intuition behind why Stein's paradox only applies in dimensions $\ge 3$ | @cardinal gave a great answer (+1), but the whole issue remains mysterious unless one is familiar with the proofs (and I am not). So I think the question remains as to what is an intuitive reason that Stein's paradox does not appear in $\mathbb R$ and $\mathbb R^2$.
I find very helpful a regression perspective offered ... | Intuition behind why Stein's paradox only applies in dimensions $\ge 3$ | @cardinal gave a great answer (+1), but the whole issue remains mysterious unless one is familiar with the proofs (and I am not). So I think the question remains as to what is an intuitive reason that | Intuition behind why Stein's paradox only applies in dimensions $\ge 3$
@cardinal gave a great answer (+1), but the whole issue remains mysterious unless one is familiar with the proofs (and I am not). So I think the question remains as to what is an intuitive reason that Stein's paradox does not appear in $\mathbb R$ ... | Intuition behind why Stein's paradox only applies in dimensions $\ge 3$
@cardinal gave a great answer (+1), but the whole issue remains mysterious unless one is familiar with the proofs (and I am not). So I think the question remains as to what is an intuitive reason that |
4,514 | How to simulate artificial data for logistic regression? | No. The response variable $y_i$ is a Bernoulli random variable taking value $1$ with probability $pr(i)$.
> set.seed(666)
> x1 = rnorm(1000) # some continuous variables
> x2 = rnorm(1000)
> z = 1 + 2*x1 + 3*x2 # linear combination with a bias
> pr = 1/(1+exp(-z)) # pass through an inv-logit fu... | How to simulate artificial data for logistic regression? | No. The response variable $y_i$ is a Bernoulli random variable taking value $1$ with probability $pr(i)$.
> set.seed(666)
> x1 = rnorm(1000) # some continuous variables
> x2 = rnorm(1000)
> | How to simulate artificial data for logistic regression?
No. The response variable $y_i$ is a Bernoulli random variable taking value $1$ with probability $pr(i)$.
> set.seed(666)
> x1 = rnorm(1000) # some continuous variables
> x2 = rnorm(1000)
> z = 1 + 2*x1 + 3*x2 # linear combination with a bias
> ... | How to simulate artificial data for logistic regression?
No. The response variable $y_i$ is a Bernoulli random variable taking value $1$ with probability $pr(i)$.
> set.seed(666)
> x1 = rnorm(1000) # some continuous variables
> x2 = rnorm(1000)
> |
4,515 | How to simulate artificial data for logistic regression? | LogisticRegression is suitable for fitting if probabilities or proportions are provided as the targets, not only 0/1 outcomes.
import numpy as np
import pandas as pd
def logistic(x, b, noise=None):
L = x.T.dot(b)
if noise is not None:
L = L+noise
return 1/(1+np.exp(-L))
x = np.arange(-10., 10, 0.05... | How to simulate artificial data for logistic regression? | LogisticRegression is suitable for fitting if probabilities or proportions are provided as the targets, not only 0/1 outcomes.
import numpy as np
import pandas as pd
def logistic(x, b, noise=None):
| How to simulate artificial data for logistic regression?
LogisticRegression is suitable for fitting if probabilities or proportions are provided as the targets, not only 0/1 outcomes.
import numpy as np
import pandas as pd
def logistic(x, b, noise=None):
L = x.T.dot(b)
if noise is not None:
L = L+noise
... | How to simulate artificial data for logistic regression?
LogisticRegression is suitable for fitting if probabilities or proportions are provided as the targets, not only 0/1 outcomes.
import numpy as np
import pandas as pd
def logistic(x, b, noise=None):
|
4,516 | Why do we use ReLU in neural networks and how do we use it? | The ReLU function is $f(x)=\max(0, x).$ Usually this is applied element-wise to the output of some other function, such as a matrix-vector product. In MLP usages, rectifier units replace all other activation functions except perhaps the readout layer. But I suppose you could mix-and-match them if you'd like.
One way Re... | Why do we use ReLU in neural networks and how do we use it? | The ReLU function is $f(x)=\max(0, x).$ Usually this is applied element-wise to the output of some other function, such as a matrix-vector product. In MLP usages, rectifier units replace all other act | Why do we use ReLU in neural networks and how do we use it?
The ReLU function is $f(x)=\max(0, x).$ Usually this is applied element-wise to the output of some other function, such as a matrix-vector product. In MLP usages, rectifier units replace all other activation functions except perhaps the readout layer. But I su... | Why do we use ReLU in neural networks and how do we use it?
The ReLU function is $f(x)=\max(0, x).$ Usually this is applied element-wise to the output of some other function, such as a matrix-vector product. In MLP usages, rectifier units replace all other act |
4,517 | Why do we use ReLU in neural networks and how do we use it? | One important thing to point out is that ReLU is idempotent. Given that ReLU is $\rho(x) = \max(0, x)$, it's easy to see that $\rho \circ \rho \circ \rho \circ \dots \circ \rho = \rho$ is true for any finite composition. This property is very important for deep neural networks, because each layer in the network applies... | Why do we use ReLU in neural networks and how do we use it? | One important thing to point out is that ReLU is idempotent. Given that ReLU is $\rho(x) = \max(0, x)$, it's easy to see that $\rho \circ \rho \circ \rho \circ \dots \circ \rho = \rho$ is true for any | Why do we use ReLU in neural networks and how do we use it?
One important thing to point out is that ReLU is idempotent. Given that ReLU is $\rho(x) = \max(0, x)$, it's easy to see that $\rho \circ \rho \circ \rho \circ \dots \circ \rho = \rho$ is true for any finite composition. This property is very important for dee... | Why do we use ReLU in neural networks and how do we use it?
One important thing to point out is that ReLU is idempotent. Given that ReLU is $\rho(x) = \max(0, x)$, it's easy to see that $\rho \circ \rho \circ \rho \circ \dots \circ \rho = \rho$ is true for any |
4,518 | Why do we use ReLU in neural networks and how do we use it? | Why do we use ReLUs? We use ReLUs for the same reason we use any other non-linear activation function: To achieve a non-linear transformation of the data.
Why do we need non-linear transformations? We apply non-linear transformations in the hope that the transformed data will be (close to) linear (for regression) or (c... | Why do we use ReLU in neural networks and how do we use it? | Why do we use ReLUs? We use ReLUs for the same reason we use any other non-linear activation function: To achieve a non-linear transformation of the data.
Why do we need non-linear transformations? We | Why do we use ReLU in neural networks and how do we use it?
Why do we use ReLUs? We use ReLUs for the same reason we use any other non-linear activation function: To achieve a non-linear transformation of the data.
Why do we need non-linear transformations? We apply non-linear transformations in the hope that the trans... | Why do we use ReLU in neural networks and how do we use it?
Why do we use ReLUs? We use ReLUs for the same reason we use any other non-linear activation function: To achieve a non-linear transformation of the data.
Why do we need non-linear transformations? We |
4,519 | Why do we use ReLU in neural networks and how do we use it? | ReLU is a literal switch. With an electrical switch 1 volt in gives 1 volt out, n volts in gives n volts out when on. On/Off when you decide to switch at zero gives exactly the same graph as ReLU.
The weighted sum (dot product) of a number of weighted sums is still a linear system.
For a particular input the ReLU sw... | Why do we use ReLU in neural networks and how do we use it? | ReLU is a literal switch. With an electrical switch 1 volt in gives 1 volt out, n volts in gives n volts out when on. On/Off when you decide to switch at zero gives exactly the same graph as ReLU.
| Why do we use ReLU in neural networks and how do we use it?
ReLU is a literal switch. With an electrical switch 1 volt in gives 1 volt out, n volts in gives n volts out when on. On/Off when you decide to switch at zero gives exactly the same graph as ReLU.
The weighted sum (dot product) of a number of weighted sums ... | Why do we use ReLU in neural networks and how do we use it?
ReLU is a literal switch. With an electrical switch 1 volt in gives 1 volt out, n volts in gives n volts out when on. On/Off when you decide to switch at zero gives exactly the same graph as ReLU.
|
4,520 | Why do we use ReLU in neural networks and how do we use it? | ReLU is the max function(x,0) with input x e.g. matrix from a convolved image. ReLU then sets all negative values in the matrix x to zero and all other values are kept constant.
ReLU is computed after the convolution and is a nonlinear activation function like tanh or sigmoid.
Softmax is a classifier at the end of the... | Why do we use ReLU in neural networks and how do we use it? | ReLU is the max function(x,0) with input x e.g. matrix from a convolved image. ReLU then sets all negative values in the matrix x to zero and all other values are kept constant.
ReLU is computed afte | Why do we use ReLU in neural networks and how do we use it?
ReLU is the max function(x,0) with input x e.g. matrix from a convolved image. ReLU then sets all negative values in the matrix x to zero and all other values are kept constant.
ReLU is computed after the convolution and is a nonlinear activation function lik... | Why do we use ReLU in neural networks and how do we use it?
ReLU is the max function(x,0) with input x e.g. matrix from a convolved image. ReLU then sets all negative values in the matrix x to zero and all other values are kept constant.
ReLU is computed afte |
4,521 | Why do we use ReLU in neural networks and how do we use it? | ReLU is probably one of the simplest nonlinear function possible. A step function is simpler. However, a step function has the first derivative (gradient) zero everywhere but in one point, at which it has an infinite gradient. ReLU has a finite derivative (gradient) everywhere. It has an infinite second derivative in o... | Why do we use ReLU in neural networks and how do we use it? | ReLU is probably one of the simplest nonlinear function possible. A step function is simpler. However, a step function has the first derivative (gradient) zero everywhere but in one point, at which it | Why do we use ReLU in neural networks and how do we use it?
ReLU is probably one of the simplest nonlinear function possible. A step function is simpler. However, a step function has the first derivative (gradient) zero everywhere but in one point, at which it has an infinite gradient. ReLU has a finite derivative (gra... | Why do we use ReLU in neural networks and how do we use it?
ReLU is probably one of the simplest nonlinear function possible. A step function is simpler. However, a step function has the first derivative (gradient) zero everywhere but in one point, at which it |
4,522 | Regularization methods for logistic regression | Yes, Regularization can be used in all linear methods, including both regression and classification. I would like to show you that there are not too much difference between regression and classification: the only difference is the loss function.
Specifically, there are three major components of linear method, Loss Func... | Regularization methods for logistic regression | Yes, Regularization can be used in all linear methods, including both regression and classification. I would like to show you that there are not too much difference between regression and classificati | Regularization methods for logistic regression
Yes, Regularization can be used in all linear methods, including both regression and classification. I would like to show you that there are not too much difference between regression and classification: the only difference is the loss function.
Specifically, there are thr... | Regularization methods for logistic regression
Yes, Regularization can be used in all linear methods, including both regression and classification. I would like to show you that there are not too much difference between regression and classificati |
4,523 | Regularization methods for logistic regression | A shrinkage/regularization method that was originally proposed for logistic regression based on considerations of higher order asymptotic was Firth logistic regression... some while before all of these talks about lasso and what not started, although after ridge regression risen and subsided in popularity through 1970s... | Regularization methods for logistic regression | A shrinkage/regularization method that was originally proposed for logistic regression based on considerations of higher order asymptotic was Firth logistic regression... some while before all of thes | Regularization methods for logistic regression
A shrinkage/regularization method that was originally proposed for logistic regression based on considerations of higher order asymptotic was Firth logistic regression... some while before all of these talks about lasso and what not started, although after ridge regression... | Regularization methods for logistic regression
A shrinkage/regularization method that was originally proposed for logistic regression based on considerations of higher order asymptotic was Firth logistic regression... some while before all of thes |
4,524 | Regularization methods for logistic regression | Yes, it is applicable to logistic regression. In R, using glmnet, you simply specify the appropriate family which is "binomial" for logistic regression. There are a couple of others (poison, multinomial, etc) that you can specify depending on your data and the problem you are addressing. | Regularization methods for logistic regression | Yes, it is applicable to logistic regression. In R, using glmnet, you simply specify the appropriate family which is "binomial" for logistic regression. There are a couple of others (poison, multinomi | Regularization methods for logistic regression
Yes, it is applicable to logistic regression. In R, using glmnet, you simply specify the appropriate family which is "binomial" for logistic regression. There are a couple of others (poison, multinomial, etc) that you can specify depending on your data and the problem you ... | Regularization methods for logistic regression
Yes, it is applicable to logistic regression. In R, using glmnet, you simply specify the appropriate family which is "binomial" for logistic regression. There are a couple of others (poison, multinomi |
4,525 | What is the distribution of the sum of non i.i.d. gaussian variates? | See my comment on probabilityislogic's answer to this question. Here,
$$
\begin{align*}
X + Y &\sim N(\mu_X + \mu_Y,\; \sigma_X^2 + \sigma_Y^2 + 2\sigma_{X,Y})\\
aX + bY &\sim N(a\mu_X + b\mu_Y,\; a^2\sigma_X^2 + b^2\sigma_Y^2 + 2ab\sigma_{X,Y})
\end{align*}
$$
where $\sigma_{X,Y}$ is the covariance of $X$ and $Y$. ... | What is the distribution of the sum of non i.i.d. gaussian variates? | See my comment on probabilityislogic's answer to this question. Here,
$$
\begin{align*}
X + Y &\sim N(\mu_X + \mu_Y,\; \sigma_X^2 + \sigma_Y^2 + 2\sigma_{X,Y})\\
aX + bY &\sim N(a\mu_X + b\mu_Y,\; a | What is the distribution of the sum of non i.i.d. gaussian variates?
See my comment on probabilityislogic's answer to this question. Here,
$$
\begin{align*}
X + Y &\sim N(\mu_X + \mu_Y,\; \sigma_X^2 + \sigma_Y^2 + 2\sigma_{X,Y})\\
aX + bY &\sim N(a\mu_X + b\mu_Y,\; a^2\sigma_X^2 + b^2\sigma_Y^2 + 2ab\sigma_{X,Y})
\en... | What is the distribution of the sum of non i.i.d. gaussian variates?
See my comment on probabilityislogic's answer to this question. Here,
$$
\begin{align*}
X + Y &\sim N(\mu_X + \mu_Y,\; \sigma_X^2 + \sigma_Y^2 + 2\sigma_{X,Y})\\
aX + bY &\sim N(a\mu_X + b\mu_Y,\; a |
4,526 | What is the distribution of the sum of non i.i.d. gaussian variates? | @dilip's answer is sufficient, but I just thought I'd add some details on how you get to the result. We can use the method of characteristic functions. For any $d$-dimensional multivariate normal distribution $X\sim N_{d}(\mu,\Sigma)$ where $\mu=(\mu_1,\dots,\mu_d)^T$ and $\Sigma_{jk}=cov(X_j,X_k)\;\;j,k=1,\dots,d$, ... | What is the distribution of the sum of non i.i.d. gaussian variates? | @dilip's answer is sufficient, but I just thought I'd add some details on how you get to the result. We can use the method of characteristic functions. For any $d$-dimensional multivariate normal di | What is the distribution of the sum of non i.i.d. gaussian variates?
@dilip's answer is sufficient, but I just thought I'd add some details on how you get to the result. We can use the method of characteristic functions. For any $d$-dimensional multivariate normal distribution $X\sim N_{d}(\mu,\Sigma)$ where $\mu=(\m... | What is the distribution of the sum of non i.i.d. gaussian variates?
@dilip's answer is sufficient, but I just thought I'd add some details on how you get to the result. We can use the method of characteristic functions. For any $d$-dimensional multivariate normal di |
4,527 | How to calculate relative error when the true value is zero? | There are many alternatives, depending on the purpose.
A common one is the "Relative Percent Difference," or RPD, used in laboratory quality control procedures. Although you can find many seemingly different formulas, they all come down to comparing the difference of two values to their average magnitude:
$$d_1(x,y) ... | How to calculate relative error when the true value is zero? | There are many alternatives, depending on the purpose.
A common one is the "Relative Percent Difference," or RPD, used in laboratory quality control procedures. Although you can find many seemingly | How to calculate relative error when the true value is zero?
There are many alternatives, depending on the purpose.
A common one is the "Relative Percent Difference," or RPD, used in laboratory quality control procedures. Although you can find many seemingly different formulas, they all come down to comparing the dif... | How to calculate relative error when the true value is zero?
There are many alternatives, depending on the purpose.
A common one is the "Relative Percent Difference," or RPD, used in laboratory quality control procedures. Although you can find many seemingly |
4,528 | How to calculate relative error when the true value is zero? | First, note that you typically take the absolute value in computing the relative error.
A common solution to the problem is to compute
$$\text{relative error}=\frac{\left| x_{\text{true}}- x_{\text{test}} \right|}{1+\left|x_{\text{true}} \right|} .$$ | How to calculate relative error when the true value is zero? | First, note that you typically take the absolute value in computing the relative error.
A common solution to the problem is to compute
$$\text{relative error}=\frac{\left| x_{\text{true}}- x_{\text{ | How to calculate relative error when the true value is zero?
First, note that you typically take the absolute value in computing the relative error.
A common solution to the problem is to compute
$$\text{relative error}=\frac{\left| x_{\text{true}}- x_{\text{test}} \right|}{1+\left|x_{\text{true}} \right|} .$$ | How to calculate relative error when the true value is zero?
First, note that you typically take the absolute value in computing the relative error.
A common solution to the problem is to compute
$$\text{relative error}=\frac{\left| x_{\text{true}}- x_{\text{ |
4,529 | How to calculate relative error when the true value is zero? | Finding MAPE,
It is very debatable topic and many opensource contributors have discussed on the above topic. The most efficient approach till now is followed by the developers. Please refer to this PR to know more. | How to calculate relative error when the true value is zero? | Finding MAPE,
It is very debatable topic and many opensource contributors have discussed on the above topic. The most efficient approach till now is followed by the developers. Please refer to this PR | How to calculate relative error when the true value is zero?
Finding MAPE,
It is very debatable topic and many opensource contributors have discussed on the above topic. The most efficient approach till now is followed by the developers. Please refer to this PR to know more. | How to calculate relative error when the true value is zero?
Finding MAPE,
It is very debatable topic and many opensource contributors have discussed on the above topic. The most efficient approach till now is followed by the developers. Please refer to this PR |
4,530 | How to calculate relative error when the true value is zero? | I was a bit confused on this for a while. In the end, its because if you are trying to measure relative error with respect to zero then you are trying to force something that simply does not exist.
If you think about it, you're comparing apples to oranges when you compare relative error to the error measured from zero,... | How to calculate relative error when the true value is zero? | I was a bit confused on this for a while. In the end, its because if you are trying to measure relative error with respect to zero then you are trying to force something that simply does not exist.
If | How to calculate relative error when the true value is zero?
I was a bit confused on this for a while. In the end, its because if you are trying to measure relative error with respect to zero then you are trying to force something that simply does not exist.
If you think about it, you're comparing apples to oranges whe... | How to calculate relative error when the true value is zero?
I was a bit confused on this for a while. In the end, its because if you are trying to measure relative error with respect to zero then you are trying to force something that simply does not exist.
If |
4,531 | What are the values p, d, q, in ARIMA? | What does ARIMA(1, 0, 12) mean?
Specifically for your model, ARIMA(1, 0, 12) means that it you are describing some response variable (Y) by combining a 1st order Auto-Regressive model and a 12th order Moving Average model. A good way to think about it is (AR, I, MA). This makes your model look the following, in simple... | What are the values p, d, q, in ARIMA? | What does ARIMA(1, 0, 12) mean?
Specifically for your model, ARIMA(1, 0, 12) means that it you are describing some response variable (Y) by combining a 1st order Auto-Regressive model and a 12th orde | What are the values p, d, q, in ARIMA?
What does ARIMA(1, 0, 12) mean?
Specifically for your model, ARIMA(1, 0, 12) means that it you are describing some response variable (Y) by combining a 1st order Auto-Regressive model and a 12th order Moving Average model. A good way to think about it is (AR, I, MA). This makes y... | What are the values p, d, q, in ARIMA?
What does ARIMA(1, 0, 12) mean?
Specifically for your model, ARIMA(1, 0, 12) means that it you are describing some response variable (Y) by combining a 1st order Auto-Regressive model and a 12th orde |
4,532 | What are the values p, d, q, in ARIMA? | order(p,d,q) means, that you have an ARIMA(p, d, q) model: $\phi(B)(1-B)^d X_t=\theta(B)Z_t$, where $B$ is a lag operator and $\phi(B)=1-\phi_1B-\dots-\phi_pB^p$ also $\theta(B)=1+\theta_1B+\dots+\theta_qB^q$.
The best way to find p, d, q values in R is to use auto.arima function from library(forecast). For example, au... | What are the values p, d, q, in ARIMA? | order(p,d,q) means, that you have an ARIMA(p, d, q) model: $\phi(B)(1-B)^d X_t=\theta(B)Z_t$, where $B$ is a lag operator and $\phi(B)=1-\phi_1B-\dots-\phi_pB^p$ also $\theta(B)=1+\theta_1B+\dots+\the | What are the values p, d, q, in ARIMA?
order(p,d,q) means, that you have an ARIMA(p, d, q) model: $\phi(B)(1-B)^d X_t=\theta(B)Z_t$, where $B$ is a lag operator and $\phi(B)=1-\phi_1B-\dots-\phi_pB^p$ also $\theta(B)=1+\theta_1B+\dots+\theta_qB^q$.
The best way to find p, d, q values in R is to use auto.arima function ... | What are the values p, d, q, in ARIMA?
order(p,d,q) means, that you have an ARIMA(p, d, q) model: $\phi(B)(1-B)^d X_t=\theta(B)Z_t$, where $B$ is a lag operator and $\phi(B)=1-\phi_1B-\dots-\phi_pB^p$ also $\theta(B)=1+\theta_1B+\dots+\the |
4,533 | What are the values p, d, q, in ARIMA? | Simply put the Autoregressive Integrated Moving Average (ARIMA) tries to model a time series where your time series in question, y, can be explained by its own lagged values (Autoregressive part) and error terms (Moving Average part). The "Integrated" part of the model (the "I" in "ARIMA") refers to how many times the ... | What are the values p, d, q, in ARIMA? | Simply put the Autoregressive Integrated Moving Average (ARIMA) tries to model a time series where your time series in question, y, can be explained by its own lagged values (Autoregressive part) and | What are the values p, d, q, in ARIMA?
Simply put the Autoregressive Integrated Moving Average (ARIMA) tries to model a time series where your time series in question, y, can be explained by its own lagged values (Autoregressive part) and error terms (Moving Average part). The "Integrated" part of the model (the "I" in... | What are the values p, d, q, in ARIMA?
Simply put the Autoregressive Integrated Moving Average (ARIMA) tries to model a time series where your time series in question, y, can be explained by its own lagged values (Autoregressive part) and |
4,534 | Standard deviation of standard deviation | Let $X_1, ..., X_n \sim N(\mu, \sigma^2)$. As shown in this thread, the standard deviation of the sample standard deviation,
$$
s = \sqrt{ \frac{1}{n-1} \sum_{i=1}^{n} (X_i - \overline{X}) }, $$
is
$$ {\rm SD}(s) = \sqrt{ E \left( [E(s)- s]^2 \right) } = \sigma \sqrt{ 1 - \frac{2}{n-1} \cdot \left( \frac{ \Gamma(n/... | Standard deviation of standard deviation | Let $X_1, ..., X_n \sim N(\mu, \sigma^2)$. As shown in this thread, the standard deviation of the sample standard deviation,
$$
s = \sqrt{ \frac{1}{n-1} \sum_{i=1}^{n} (X_i - \overline{X}) }, $$
is | Standard deviation of standard deviation
Let $X_1, ..., X_n \sim N(\mu, \sigma^2)$. As shown in this thread, the standard deviation of the sample standard deviation,
$$
s = \sqrt{ \frac{1}{n-1} \sum_{i=1}^{n} (X_i - \overline{X}) }, $$
is
$$ {\rm SD}(s) = \sqrt{ E \left( [E(s)- s]^2 \right) } = \sigma \sqrt{ 1 - \f... | Standard deviation of standard deviation
Let $X_1, ..., X_n \sim N(\mu, \sigma^2)$. As shown in this thread, the standard deviation of the sample standard deviation,
$$
s = \sqrt{ \frac{1}{n-1} \sum_{i=1}^{n} (X_i - \overline{X}) }, $$
is |
4,535 | Standard deviation of standard deviation | Assume you observe $X_1,\dots,X_n$ iid from a normal with mean zero and variance $\sigma^2$. The (empirical) standard deviation is the square root of the estimator $\hat{\sigma}^2$ of $\sigma^2$ (unbiased or not that is not the question). As an estimator (obtained with $X_1,\dots,X_n$), $\hat{\sigma}$ has a variance th... | Standard deviation of standard deviation | Assume you observe $X_1,\dots,X_n$ iid from a normal with mean zero and variance $\sigma^2$. The (empirical) standard deviation is the square root of the estimator $\hat{\sigma}^2$ of $\sigma^2$ (unbi | Standard deviation of standard deviation
Assume you observe $X_1,\dots,X_n$ iid from a normal with mean zero and variance $\sigma^2$. The (empirical) standard deviation is the square root of the estimator $\hat{\sigma}^2$ of $\sigma^2$ (unbiased or not that is not the question). As an estimator (obtained with $X_1,\dot... | Standard deviation of standard deviation
Assume you observe $X_1,\dots,X_n$ iid from a normal with mean zero and variance $\sigma^2$. The (empirical) standard deviation is the square root of the estimator $\hat{\sigma}^2$ of $\sigma^2$ (unbi |
4,536 | Standard deviation of standard deviation | @Macro provided a great mathematical explanation with equation to compute. Here is a more general explation for less mathematical people.
I think the terminology "SD of SD" is confusing to many. It is easier to think about the confidence interval of a SD. How precise is the standard deviation you compute from a sample?... | Standard deviation of standard deviation | @Macro provided a great mathematical explanation with equation to compute. Here is a more general explation for less mathematical people.
I think the terminology "SD of SD" is confusing to many. It is | Standard deviation of standard deviation
@Macro provided a great mathematical explanation with equation to compute. Here is a more general explation for less mathematical people.
I think the terminology "SD of SD" is confusing to many. It is easier to think about the confidence interval of a SD. How precise is the stan... | Standard deviation of standard deviation
@Macro provided a great mathematical explanation with equation to compute. Here is a more general explation for less mathematical people.
I think the terminology "SD of SD" is confusing to many. It is |
4,537 | Normalization vs. scaling | I am not aware of an "official" definition and even if there it is, you shouldn't trust it as you will see it being used inconsistently in practice.
This being said, scaling in statistics usually means a linear transformation of the form
$f(x) = ax+b$.
Normalizing can either mean applying a transformation so that you ... | Normalization vs. scaling | I am not aware of an "official" definition and even if there it is, you shouldn't trust it as you will see it being used inconsistently in practice.
This being said, scaling in statistics usually mean | Normalization vs. scaling
I am not aware of an "official" definition and even if there it is, you shouldn't trust it as you will see it being used inconsistently in practice.
This being said, scaling in statistics usually means a linear transformation of the form
$f(x) = ax+b$.
Normalizing can either mean applying a t... | Normalization vs. scaling
I am not aware of an "official" definition and even if there it is, you shouldn't trust it as you will see it being used inconsistently in practice.
This being said, scaling in statistics usually mean |
4,538 | Normalization vs. scaling | Scaling is a personal choice about making the numbers feel right, e.g. between zero and one, or one and a hundred. For example converting data given in millimeters to meters because it's more convenient, or imperial to metric.
While normalisation is about scaling to an external 'standard' - the local norm - such as rem... | Normalization vs. scaling | Scaling is a personal choice about making the numbers feel right, e.g. between zero and one, or one and a hundred. For example converting data given in millimeters to meters because it's more convenie | Normalization vs. scaling
Scaling is a personal choice about making the numbers feel right, e.g. between zero and one, or one and a hundred. For example converting data given in millimeters to meters because it's more convenient, or imperial to metric.
While normalisation is about scaling to an external 'standard' - th... | Normalization vs. scaling
Scaling is a personal choice about making the numbers feel right, e.g. between zero and one, or one and a hundred. For example converting data given in millimeters to meters because it's more convenie |
4,539 | Normalization vs. scaling | I don't know if you mean exactly this, but I see a lot of people referring to Normalization meaning data Standardization. Standardization is transforming your data so it has mean 0 and standard deviation 1:
x <- (x - mean(x)) / sd(x)
I also see people using the term Normalization for Data Scaling, as in transforming y... | Normalization vs. scaling | I don't know if you mean exactly this, but I see a lot of people referring to Normalization meaning data Standardization. Standardization is transforming your data so it has mean 0 and standard deviat | Normalization vs. scaling
I don't know if you mean exactly this, but I see a lot of people referring to Normalization meaning data Standardization. Standardization is transforming your data so it has mean 0 and standard deviation 1:
x <- (x - mean(x)) / sd(x)
I also see people using the term Normalization for Data Sca... | Normalization vs. scaling
I don't know if you mean exactly this, but I see a lot of people referring to Normalization meaning data Standardization. Standardization is transforming your data so it has mean 0 and standard deviat |
4,540 | Normalization vs. scaling | Centering means substacting the mean of the random variable from the variables. I.e x -xi
Scalelling means dividing variable by its standard deviation. I.e xi /s
Combination of the two is called normalization or standization. I.e x-xi/s | Normalization vs. scaling | Centering means substacting the mean of the random variable from the variables. I.e x -xi
Scalelling means dividing variable by its standard deviation. I.e xi /s
Combination of the two is called norma | Normalization vs. scaling
Centering means substacting the mean of the random variable from the variables. I.e x -xi
Scalelling means dividing variable by its standard deviation. I.e xi /s
Combination of the two is called normalization or standization. I.e x-xi/s | Normalization vs. scaling
Centering means substacting the mean of the random variable from the variables. I.e x -xi
Scalelling means dividing variable by its standard deviation. I.e xi /s
Combination of the two is called norma |
4,541 | How does centering make a difference in PCA (for SVD and eigen decomposition)? | As you remarked yourself and as explained by @ttnphns in the comments, computing covariance matrix implicitly performs centering: variance, by definition, is the average squared deviation from the mean. Centered and non-centered data will have identical covariance matrices. So if by PCA we understand the following proc... | How does centering make a difference in PCA (for SVD and eigen decomposition)? | As you remarked yourself and as explained by @ttnphns in the comments, computing covariance matrix implicitly performs centering: variance, by definition, is the average squared deviation from the mea | How does centering make a difference in PCA (for SVD and eigen decomposition)?
As you remarked yourself and as explained by @ttnphns in the comments, computing covariance matrix implicitly performs centering: variance, by definition, is the average squared deviation from the mean. Centered and non-centered data will ha... | How does centering make a difference in PCA (for SVD and eigen decomposition)?
As you remarked yourself and as explained by @ttnphns in the comments, computing covariance matrix implicitly performs centering: variance, by definition, is the average squared deviation from the mea |
4,542 | How does centering make a difference in PCA (for SVD and eigen decomposition)? | I'll try to provide a mathematical justification.
By centering, I assume you mean applying eigendecomposition on $XX^T$ instead of $(X-\mu)(X-\mu)^T$
Here $\mu$ denotes the sample mean and not the true mean, thus $\mu = \sum_i x_i / n$
Let $B = \sum_i x_ix_i^T$
Let $A = \sum_i (x_i - \mu)(x_i - \mu)^T$
$A= \sum_i (x_ix... | How does centering make a difference in PCA (for SVD and eigen decomposition)? | I'll try to provide a mathematical justification.
By centering, I assume you mean applying eigendecomposition on $XX^T$ instead of $(X-\mu)(X-\mu)^T$
Here $\mu$ denotes the sample mean and not the tru | How does centering make a difference in PCA (for SVD and eigen decomposition)?
I'll try to provide a mathematical justification.
By centering, I assume you mean applying eigendecomposition on $XX^T$ instead of $(X-\mu)(X-\mu)^T$
Here $\mu$ denotes the sample mean and not the true mean, thus $\mu = \sum_i x_i / n$
Let $... | How does centering make a difference in PCA (for SVD and eigen decomposition)?
I'll try to provide a mathematical justification.
By centering, I assume you mean applying eigendecomposition on $XX^T$ instead of $(X-\mu)(X-\mu)^T$
Here $\mu$ denotes the sample mean and not the tru |
4,543 | How does centering make a difference in PCA (for SVD and eigen decomposition)? | Carefully read through this thread which helps me greatly on understanding the whole PCA process, here is my summary (not sure if I get the true idea or not):
PCA <-> Eigen-decomposition on cov matrix -> it will find axis on whose direction the data has max spread. it doesn't matter whether we center the data data befo... | How does centering make a difference in PCA (for SVD and eigen decomposition)? | Carefully read through this thread which helps me greatly on understanding the whole PCA process, here is my summary (not sure if I get the true idea or not):
PCA <-> Eigen-decomposition on cov matrix | How does centering make a difference in PCA (for SVD and eigen decomposition)?
Carefully read through this thread which helps me greatly on understanding the whole PCA process, here is my summary (not sure if I get the true idea or not):
PCA <-> Eigen-decomposition on cov matrix -> it will find axis on whose direction ... | How does centering make a difference in PCA (for SVD and eigen decomposition)?
Carefully read through this thread which helps me greatly on understanding the whole PCA process, here is my summary (not sure if I get the true idea or not):
PCA <-> Eigen-decomposition on cov matrix |
4,544 | Why are regression problems called "regression" problems? | The term "regression" was used by Francis Galton in his 1886 paper "Regression towards mediocrity in hereditary stature". To my knowledge he only used the term in the context of regression toward the mean. The term was then adopted by others to get more or less the meaning it has today as a general statistical method. | Why are regression problems called "regression" problems? | The term "regression" was used by Francis Galton in his 1886 paper "Regression towards mediocrity in hereditary stature". To my knowledge he only used the term in the context of regression toward the | Why are regression problems called "regression" problems?
The term "regression" was used by Francis Galton in his 1886 paper "Regression towards mediocrity in hereditary stature". To my knowledge he only used the term in the context of regression toward the mean. The term was then adopted by others to get more or less ... | Why are regression problems called "regression" problems?
The term "regression" was used by Francis Galton in his 1886 paper "Regression towards mediocrity in hereditary stature". To my knowledge he only used the term in the context of regression toward the |
4,545 | Why are regression problems called "regression" problems? | @Mark White mentioned the link already but for those of you who do not have much time to check the link, here's the exact properly referenced answer:
Origin of 'regression'
The term "regression" was coined by Francis Galton in the 19th century to describe a biological phenomenon. The phenomenon was that the heights of ... | Why are regression problems called "regression" problems? | @Mark White mentioned the link already but for those of you who do not have much time to check the link, here's the exact properly referenced answer:
Origin of 'regression'
The term "regression" was c | Why are regression problems called "regression" problems?
@Mark White mentioned the link already but for those of you who do not have much time to check the link, here's the exact properly referenced answer:
Origin of 'regression'
The term "regression" was coined by Francis Galton in the 19th century to describe a biol... | Why are regression problems called "regression" problems?
@Mark White mentioned the link already but for those of you who do not have much time to check the link, here's the exact properly referenced answer:
Origin of 'regression'
The term "regression" was c |
4,546 | Why are regression problems called "regression" problems? | As opposed to progressing, we are falling back to the mean, i.e. regressing. Hence the term regression ! I think its something that got picked up and stuck. | Why are regression problems called "regression" problems? | As opposed to progressing, we are falling back to the mean, i.e. regressing. Hence the term regression ! I think its something that got picked up and stuck. | Why are regression problems called "regression" problems?
As opposed to progressing, we are falling back to the mean, i.e. regressing. Hence the term regression ! I think its something that got picked up and stuck. | Why are regression problems called "regression" problems?
As opposed to progressing, we are falling back to the mean, i.e. regressing. Hence the term regression ! I think its something that got picked up and stuck. |
4,547 | Why are regression problems called "regression" problems? | I arrived here via a search for how a regression got its name. Here are the interesting parts of what I found (mostly from wikipedia.)
The term "regression" was coined by Francis Galton in the nineteenth century to describe a biological phenomenon. The phenomenon was that the heights of descendants of tall ancestors t... | Why are regression problems called "regression" problems? | I arrived here via a search for how a regression got its name. Here are the interesting parts of what I found (mostly from wikipedia.)
The term "regression" was coined by Francis Galton in the ninete | Why are regression problems called "regression" problems?
I arrived here via a search for how a regression got its name. Here are the interesting parts of what I found (mostly from wikipedia.)
The term "regression" was coined by Francis Galton in the nineteenth century to describe a biological phenomenon. The phenomen... | Why are regression problems called "regression" problems?
I arrived here via a search for how a regression got its name. Here are the interesting parts of what I found (mostly from wikipedia.)
The term "regression" was coined by Francis Galton in the ninete |
4,548 | Why are regression problems called "regression" problems? | "Regression" comes from "regress" which in turn comes from latin "regressus" - to go back (to something).
In that sense, regression is the technique that allows "to go back" from messy, hard to interpret data, to a clearer and more meaningful model. As a physicist, I like the idea, as physicists see natural phenomena ... | Why are regression problems called "regression" problems? | "Regression" comes from "regress" which in turn comes from latin "regressus" - to go back (to something).
In that sense, regression is the technique that allows "to go back" from messy, hard to inter | Why are regression problems called "regression" problems?
"Regression" comes from "regress" which in turn comes from latin "regressus" - to go back (to something).
In that sense, regression is the technique that allows "to go back" from messy, hard to interpret data, to a clearer and more meaningful model. As a physic... | Why are regression problems called "regression" problems?
"Regression" comes from "regress" which in turn comes from latin "regressus" - to go back (to something).
In that sense, regression is the technique that allows "to go back" from messy, hard to inter |
4,549 | How to visualize a fitted multiple regression model? | There is nothing wrong with your current strategy. If you have a multiple regression model with only two explanatory variables then you could try to make a 3D-ish plot that displays the predicted regression plane, but most software don't make this easy to do. Another possibility is to use a coplot (see also: coplot i... | How to visualize a fitted multiple regression model? | There is nothing wrong with your current strategy. If you have a multiple regression model with only two explanatory variables then you could try to make a 3D-ish plot that displays the predicted reg | How to visualize a fitted multiple regression model?
There is nothing wrong with your current strategy. If you have a multiple regression model with only two explanatory variables then you could try to make a 3D-ish plot that displays the predicted regression plane, but most software don't make this easy to do. Anoth... | How to visualize a fitted multiple regression model?
There is nothing wrong with your current strategy. If you have a multiple regression model with only two explanatory variables then you could try to make a 3D-ish plot that displays the predicted reg |
4,550 | How to visualize a fitted multiple regression model? | Here is a web-based, interactive tool for plotting regression results in three dimensions.
This 3-D plot works with one dependent variable and two explanatory variables. You can also set the intercept to zero (i.e., remove the intercept from the regression equation).
This page shows a 3D scatter plot without the fitted... | How to visualize a fitted multiple regression model? | Here is a web-based, interactive tool for plotting regression results in three dimensions.
This 3-D plot works with one dependent variable and two explanatory variables. You can also set the intercept | How to visualize a fitted multiple regression model?
Here is a web-based, interactive tool for plotting regression results in three dimensions.
This 3-D plot works with one dependent variable and two explanatory variables. You can also set the intercept to zero (i.e., remove the intercept from the regression equation).... | How to visualize a fitted multiple regression model?
Here is a web-based, interactive tool for plotting regression results in three dimensions.
This 3-D plot works with one dependent variable and two explanatory variables. You can also set the intercept |
4,551 | How to visualize a fitted multiple regression model? | To visualize the model, rather than the data, JMP uses an interactive "profiler" plot. Here's a static view.
And here's a link to a dynamic view.
It's similar to your scatter plot idea and can be combined with it. The idea is that each frame shows a slice of the model for the corresponding X and Y variables with the o... | How to visualize a fitted multiple regression model? | To visualize the model, rather than the data, JMP uses an interactive "profiler" plot. Here's a static view.
And here's a link to a dynamic view.
It's similar to your scatter plot idea and can be com | How to visualize a fitted multiple regression model?
To visualize the model, rather than the data, JMP uses an interactive "profiler" plot. Here's a static view.
And here's a link to a dynamic view.
It's similar to your scatter plot idea and can be combined with it. The idea is that each frame shows a slice of the mod... | How to visualize a fitted multiple regression model?
To visualize the model, rather than the data, JMP uses an interactive "profiler" plot. Here's a static view.
And here's a link to a dynamic view.
It's similar to your scatter plot idea and can be com |
4,552 | How to visualize a fitted multiple regression model? | See the R rms package and the RMS course notes, in particular the nomogram and Predict functions to obtain nomograms and partial effect plots. The summary.rms function computes one-number effect summaries of each predictor (inter-quartile range effects). Nomograms provide the most complete single representation of r... | How to visualize a fitted multiple regression model? | See the R rms package and the RMS course notes, in particular the nomogram and Predict functions to obtain nomograms and partial effect plots. The summary.rms function computes one-number effect sum | How to visualize a fitted multiple regression model?
See the R rms package and the RMS course notes, in particular the nomogram and Predict functions to obtain nomograms and partial effect plots. The summary.rms function computes one-number effect summaries of each predictor (inter-quartile range effects). Nomograms... | How to visualize a fitted multiple regression model?
See the R rms package and the RMS course notes, in particular the nomogram and Predict functions to obtain nomograms and partial effect plots. The summary.rms function computes one-number effect sum |
4,553 | Is it possible to do time-series clustering based on curve shape? | Several directions for analyzing longitudinal data were discussed in the link provided by @Jeromy, so I would suggest you to read them carefully, especially those on functional data analysis. Try googling for "Functional Clustering of Longitudinal Data", or the PACE Matlab toolbox which is specifically concerned with m... | Is it possible to do time-series clustering based on curve shape? | Several directions for analyzing longitudinal data were discussed in the link provided by @Jeromy, so I would suggest you to read them carefully, especially those on functional data analysis. Try goog | Is it possible to do time-series clustering based on curve shape?
Several directions for analyzing longitudinal data were discussed in the link provided by @Jeromy, so I would suggest you to read them carefully, especially those on functional data analysis. Try googling for "Functional Clustering of Longitudinal Data",... | Is it possible to do time-series clustering based on curve shape?
Several directions for analyzing longitudinal data were discussed in the link provided by @Jeromy, so I would suggest you to read them carefully, especially those on functional data analysis. Try goog |
4,554 | Is it possible to do time-series clustering based on curve shape? | An alternative approach was published by a stats.se regular in Wang, Xiaozhe, Kate Smith, and Rob Hyndman.
‘Characteristic-Based Clustering for Time Series Data’. Data Mining
and Knowledge Discovery 13, no. 3 (2006): 335–364.
They write:
This paper proposes a method for clustering of time series based on
their struc... | Is it possible to do time-series clustering based on curve shape? | An alternative approach was published by a stats.se regular in Wang, Xiaozhe, Kate Smith, and Rob Hyndman.
‘Characteristic-Based Clustering for Time Series Data’. Data Mining
and Knowledge Discovery | Is it possible to do time-series clustering based on curve shape?
An alternative approach was published by a stats.se regular in Wang, Xiaozhe, Kate Smith, and Rob Hyndman.
‘Characteristic-Based Clustering for Time Series Data’. Data Mining
and Knowledge Discovery 13, no. 3 (2006): 335–364.
They write:
This paper pr... | Is it possible to do time-series clustering based on curve shape?
An alternative approach was published by a stats.se regular in Wang, Xiaozhe, Kate Smith, and Rob Hyndman.
‘Characteristic-Based Clustering for Time Series Data’. Data Mining
and Knowledge Discovery |
4,555 | Is it possible to do time-series clustering based on curve shape? | You could look at the work of Eamonn Keogh (UC Riverside) on time series clustering. His website has a lot of resources. I think he provides Matlab code samples, so you'd have to translate this to R. | Is it possible to do time-series clustering based on curve shape? | You could look at the work of Eamonn Keogh (UC Riverside) on time series clustering. His website has a lot of resources. I think he provides Matlab code samples, so you'd have to translate this to R. | Is it possible to do time-series clustering based on curve shape?
You could look at the work of Eamonn Keogh (UC Riverside) on time series clustering. His website has a lot of resources. I think he provides Matlab code samples, so you'd have to translate this to R. | Is it possible to do time-series clustering based on curve shape?
You could look at the work of Eamonn Keogh (UC Riverside) on time series clustering. His website has a lot of resources. I think he provides Matlab code samples, so you'd have to translate this to R. |
4,556 | Modern successor to Exploratory Data Analysis by Tukey? | The closest thing is Cleveland's Visualizing Data. It's about Exploratory Data Analysis, it's about computer-generated visualizations, it's profound, it's a classic. | Modern successor to Exploratory Data Analysis by Tukey? | The closest thing is Cleveland's Visualizing Data. It's about Exploratory Data Analysis, it's about computer-generated visualizations, it's profound, it's a classic. | Modern successor to Exploratory Data Analysis by Tukey?
The closest thing is Cleveland's Visualizing Data. It's about Exploratory Data Analysis, it's about computer-generated visualizations, it's profound, it's a classic. | Modern successor to Exploratory Data Analysis by Tukey?
The closest thing is Cleveland's Visualizing Data. It's about Exploratory Data Analysis, it's about computer-generated visualizations, it's profound, it's a classic. |
4,557 | Modern successor to Exploratory Data Analysis by Tukey? | Well, its not an exact replica, but I found tons of useful plotting advice (and R code) in Gelman and Hill's Data Analysis using Regression and Multilevel/Hierarchical Models
In addition, his blog is often full of useful graphics advice. | Modern successor to Exploratory Data Analysis by Tukey? | Well, its not an exact replica, but I found tons of useful plotting advice (and R code) in Gelman and Hill's Data Analysis using Regression and Multilevel/Hierarchical Models
In addition, his blog is | Modern successor to Exploratory Data Analysis by Tukey?
Well, its not an exact replica, but I found tons of useful plotting advice (and R code) in Gelman and Hill's Data Analysis using Regression and Multilevel/Hierarchical Models
In addition, his blog is often full of useful graphics advice. | Modern successor to Exploratory Data Analysis by Tukey?
Well, its not an exact replica, but I found tons of useful plotting advice (and R code) in Gelman and Hill's Data Analysis using Regression and Multilevel/Hierarchical Models
In addition, his blog is |
4,558 | Modern successor to Exploratory Data Analysis by Tukey? | Interactive Graphics for Data Analysis: Principles and Examples is one I like; the book description says it "discusses exploratory data analysis (EDA) and how interactive graphical methods can help gain insights as well as generate new questions and hypotheses from datasets." | Modern successor to Exploratory Data Analysis by Tukey? | Interactive Graphics for Data Analysis: Principles and Examples is one I like; the book description says it "discusses exploratory data analysis (EDA) and how interactive graphical methods can help ga | Modern successor to Exploratory Data Analysis by Tukey?
Interactive Graphics for Data Analysis: Principles and Examples is one I like; the book description says it "discusses exploratory data analysis (EDA) and how interactive graphical methods can help gain insights as well as generate new questions and hypotheses fro... | Modern successor to Exploratory Data Analysis by Tukey?
Interactive Graphics for Data Analysis: Principles and Examples is one I like; the book description says it "discusses exploratory data analysis (EDA) and how interactive graphical methods can help ga |
4,559 | Modern successor to Exploratory Data Analysis by Tukey? | Hadley Wickham's ggplot2 book is interesting because it teaches both the Grammar of Graphics and how to use the ggplot2 software. | Modern successor to Exploratory Data Analysis by Tukey? | Hadley Wickham's ggplot2 book is interesting because it teaches both the Grammar of Graphics and how to use the ggplot2 software. | Modern successor to Exploratory Data Analysis by Tukey?
Hadley Wickham's ggplot2 book is interesting because it teaches both the Grammar of Graphics and how to use the ggplot2 software. | Modern successor to Exploratory Data Analysis by Tukey?
Hadley Wickham's ggplot2 book is interesting because it teaches both the Grammar of Graphics and how to use the ggplot2 software. |
4,560 | Modern successor to Exploratory Data Analysis by Tukey? | Ronald Pearson's Exploring Data in Engineering, the Sciences, and Medicine is worth mentioning here. Its main target readership seems to be scientists not afraid of a little mathematics who wish they knew more statistics. That is quite a large group, and one well represented here. It's a little quirky and offbeat, but ... | Modern successor to Exploratory Data Analysis by Tukey? | Ronald Pearson's Exploring Data in Engineering, the Sciences, and Medicine is worth mentioning here. Its main target readership seems to be scientists not afraid of a little mathematics who wish they | Modern successor to Exploratory Data Analysis by Tukey?
Ronald Pearson's Exploring Data in Engineering, the Sciences, and Medicine is worth mentioning here. Its main target readership seems to be scientists not afraid of a little mathematics who wish they knew more statistics. That is quite a large group, and one well ... | Modern successor to Exploratory Data Analysis by Tukey?
Ronald Pearson's Exploring Data in Engineering, the Sciences, and Medicine is worth mentioning here. Its main target readership seems to be scientists not afraid of a little mathematics who wish they |
4,561 | Modern successor to Exploratory Data Analysis by Tukey? | Also Interactive and Dynamic Graphics for Data Analysis: With Examples Using R and GGobi, Cook and Swayne
This has two chapters publicly available on the web that describe the process of data analysis, and handling missing values. There's a new book coming out by Antony Unwin soon. | Modern successor to Exploratory Data Analysis by Tukey? | Also Interactive and Dynamic Graphics for Data Analysis: With Examples Using R and GGobi, Cook and Swayne
This has two chapters publicly available on the web that describe the process of data analysis | Modern successor to Exploratory Data Analysis by Tukey?
Also Interactive and Dynamic Graphics for Data Analysis: With Examples Using R and GGobi, Cook and Swayne
This has two chapters publicly available on the web that describe the process of data analysis, and handling missing values. There's a new book coming out by ... | Modern successor to Exploratory Data Analysis by Tukey?
Also Interactive and Dynamic Graphics for Data Analysis: With Examples Using R and GGobi, Cook and Swayne
This has two chapters publicly available on the web that describe the process of data analysis |
4,562 | Modern successor to Exploratory Data Analysis by Tukey? | Claus Wilke's 2019 book "Fundamentals of Data Visualization" is another possible "modern successor." The book's preprint is still freely available online.
Like Tukey's EDA, Wilke's book is focused on exploring your data using graphs while keeping in mind the things that matter to statisticians: thinking in terms of dis... | Modern successor to Exploratory Data Analysis by Tukey? | Claus Wilke's 2019 book "Fundamentals of Data Visualization" is another possible "modern successor." The book's preprint is still freely available online.
Like Tukey's EDA, Wilke's book is focused on | Modern successor to Exploratory Data Analysis by Tukey?
Claus Wilke's 2019 book "Fundamentals of Data Visualization" is another possible "modern successor." The book's preprint is still freely available online.
Like Tukey's EDA, Wilke's book is focused on exploring your data using graphs while keeping in mind the thing... | Modern successor to Exploratory Data Analysis by Tukey?
Claus Wilke's 2019 book "Fundamentals of Data Visualization" is another possible "modern successor." The book's preprint is still freely available online.
Like Tukey's EDA, Wilke's book is focused on |
4,563 | Modern successor to Exploratory Data Analysis by Tukey? | Another couple of good books to read are Beautiful Visualization and Beautiful Data. These are edited books, there are amazingly good examples of exploring data with plots, and some absolutely appalling chapters.
Another book that has some good examples of using ggplot2 is a new one by Winston Chang | Modern successor to Exploratory Data Analysis by Tukey? | Another couple of good books to read are Beautiful Visualization and Beautiful Data. These are edited books, there are amazingly good examples of exploring data with plots, and some absolutely appalli | Modern successor to Exploratory Data Analysis by Tukey?
Another couple of good books to read are Beautiful Visualization and Beautiful Data. These are edited books, there are amazingly good examples of exploring data with plots, and some absolutely appalling chapters.
Another book that has some good examples of using ... | Modern successor to Exploratory Data Analysis by Tukey?
Another couple of good books to read are Beautiful Visualization and Beautiful Data. These are edited books, there are amazingly good examples of exploring data with plots, and some absolutely appalli |
4,564 | Modern successor to Exploratory Data Analysis by Tukey? | I think of Understanding robust and exploratory analysis by Hoaglin, Mosteller and Tukey an the companion volume on Exploring data tables and shapes as the technical follow-up to EDA.
I also see data analysis and regression, a second course in statistics by Mosteller and Tukey as follow-up to EDA. The various Clevelan... | Modern successor to Exploratory Data Analysis by Tukey? | I think of Understanding robust and exploratory analysis by Hoaglin, Mosteller and Tukey an the companion volume on Exploring data tables and shapes as the technical follow-up to EDA.
I also see data | Modern successor to Exploratory Data Analysis by Tukey?
I think of Understanding robust and exploratory analysis by Hoaglin, Mosteller and Tukey an the companion volume on Exploring data tables and shapes as the technical follow-up to EDA.
I also see data analysis and regression, a second course in statistics by Mostel... | Modern successor to Exploratory Data Analysis by Tukey?
I think of Understanding robust and exploratory analysis by Hoaglin, Mosteller and Tukey an the companion volume on Exploring data tables and shapes as the technical follow-up to EDA.
I also see data |
4,565 | How do I find peaks in a dataset? | A general approach is to smooth the data and then find peaks by comparing a local maximum filter to the smooth. In R:
argmax <- function(x, y, w=1, ...) {
require(zoo)
n <- length(y)
y.smooth <- loess(y ~ x, ...)$fitted
y.max <- rollapply(zoo(y.smooth), 2*w+1, max,
align="c... | How do I find peaks in a dataset? | A general approach is to smooth the data and then find peaks by comparing a local maximum filter to the smooth. In R:
argmax <- function(x, y, w=1, ...) {
require(zoo)
n <- length(y)
| How do I find peaks in a dataset?
A general approach is to smooth the data and then find peaks by comparing a local maximum filter to the smooth. In R:
argmax <- function(x, y, w=1, ...) {
require(zoo)
n <- length(y)
y.smooth <- loess(y ~ x, ...)$fitted
y.max <- rollapply(zoo(y.smooth), 2*w... | How do I find peaks in a dataset?
A general approach is to smooth the data and then find peaks by comparing a local maximum filter to the smooth. In R:
argmax <- function(x, y, w=1, ...) {
require(zoo)
n <- length(y)
|
4,566 | How do I find peaks in a dataset? | A classic peak detection approach in signal processing is as follows:
Filter the signal to some reasonable reasonable range, depending on
sampling rate and signal properties, e.g. for ECG, an IIR bandpass
filter @0.5-20Hz, a zero-phase filter will ensure that no phase
shift (and associated time lag) is introduced
A h... | How do I find peaks in a dataset? | A classic peak detection approach in signal processing is as follows:
Filter the signal to some reasonable reasonable range, depending on
sampling rate and signal properties, e.g. for ECG, an IIR ban | How do I find peaks in a dataset?
A classic peak detection approach in signal processing is as follows:
Filter the signal to some reasonable reasonable range, depending on
sampling rate and signal properties, e.g. for ECG, an IIR bandpass
filter @0.5-20Hz, a zero-phase filter will ensure that no phase
shift (and assoc... | How do I find peaks in a dataset?
A classic peak detection approach in signal processing is as follows:
Filter the signal to some reasonable reasonable range, depending on
sampling rate and signal properties, e.g. for ECG, an IIR ban |
4,567 | How do I find peaks in a dataset? | As I mentioned in comment if the time series appears to be periodic fitting a harmonic regression model provides a way to smooth the function and identify the peak by applying the first and second derivative tests. Huber has pointed out a nonparametric test that has advantages when there are multiple peaks and the fun... | How do I find peaks in a dataset? | As I mentioned in comment if the time series appears to be periodic fitting a harmonic regression model provides a way to smooth the function and identify the peak by applying the first and second der | How do I find peaks in a dataset?
As I mentioned in comment if the time series appears to be periodic fitting a harmonic regression model provides a way to smooth the function and identify the peak by applying the first and second derivative tests. Huber has pointed out a nonparametric test that has advantages when th... | How do I find peaks in a dataset?
As I mentioned in comment if the time series appears to be periodic fitting a harmonic regression model provides a way to smooth the function and identify the peak by applying the first and second der |
4,568 | Pandas / Statsmodel / Scikit-learn | Scikit-learn (sklearn) is the best choice for machine learning, out of the three listed. While Pandas and Statsmodels do contain some predictive learning algorithms, they are hidden/not production-ready yet. Often, as authors will work on different projects, the libraries are complimentary. For example, recently Pandas... | Pandas / Statsmodel / Scikit-learn | Scikit-learn (sklearn) is the best choice for machine learning, out of the three listed. While Pandas and Statsmodels do contain some predictive learning algorithms, they are hidden/not production-rea | Pandas / Statsmodel / Scikit-learn
Scikit-learn (sklearn) is the best choice for machine learning, out of the three listed. While Pandas and Statsmodels do contain some predictive learning algorithms, they are hidden/not production-ready yet. Often, as authors will work on different projects, the libraries are complime... | Pandas / Statsmodel / Scikit-learn
Scikit-learn (sklearn) is the best choice for machine learning, out of the three listed. While Pandas and Statsmodels do contain some predictive learning algorithms, they are hidden/not production-rea |
4,569 | Pandas / Statsmodel / Scikit-learn | I would like to qualify and clarify a bit the accepted answer.
The three packages are complementary to each other since they cover different areas, have different main objectives, or emphasize different areas in machine learning/statistics.
pandas is mainly a package to handle and operate directly on data.
scikit-lear... | Pandas / Statsmodel / Scikit-learn | I would like to qualify and clarify a bit the accepted answer.
The three packages are complementary to each other since they cover different areas, have different main objectives, or emphasize differe | Pandas / Statsmodel / Scikit-learn
I would like to qualify and clarify a bit the accepted answer.
The three packages are complementary to each other since they cover different areas, have different main objectives, or emphasize different areas in machine learning/statistics.
pandas is mainly a package to handle and op... | Pandas / Statsmodel / Scikit-learn
I would like to qualify and clarify a bit the accepted answer.
The three packages are complementary to each other since they cover different areas, have different main objectives, or emphasize differe |
4,570 | Book for reading before Elements of Statistical Learning? | I bought, but have not yet read,
S. Marsland, Machine Learning: An Algorithmic Perspective, Chapman & Hall, 2009.
However, the reviews are favorable and state that it is more suitable for beginners than other ML books that have more depth. Flipping through the pages, it looks to me to be good for me because I have ... | Book for reading before Elements of Statistical Learning? | I bought, but have not yet read,
S. Marsland, Machine Learning: An Algorithmic Perspective, Chapman & Hall, 2009.
However, the reviews are favorable and state that it is more suitable for beginner | Book for reading before Elements of Statistical Learning?
I bought, but have not yet read,
S. Marsland, Machine Learning: An Algorithmic Perspective, Chapman & Hall, 2009.
However, the reviews are favorable and state that it is more suitable for beginners than other ML books that have more depth. Flipping through t... | Book for reading before Elements of Statistical Learning?
I bought, but have not yet read,
S. Marsland, Machine Learning: An Algorithmic Perspective, Chapman & Hall, 2009.
However, the reviews are favorable and state that it is more suitable for beginner |
4,571 | Book for reading before Elements of Statistical Learning? | The authors of Elements of Statistical Learning have come out with a new book (Aug 2013) aimed at users without heavy math backgrounds. An Introduction to Statistical Learning: with Applications in R
The free PDF version of this book can currently be found here. | Book for reading before Elements of Statistical Learning? | The authors of Elements of Statistical Learning have come out with a new book (Aug 2013) aimed at users without heavy math backgrounds. An Introduction to Statistical Learning: with Applications in R
| Book for reading before Elements of Statistical Learning?
The authors of Elements of Statistical Learning have come out with a new book (Aug 2013) aimed at users without heavy math backgrounds. An Introduction to Statistical Learning: with Applications in R
The free PDF version of this book can currently be found here. | Book for reading before Elements of Statistical Learning?
The authors of Elements of Statistical Learning have come out with a new book (Aug 2013) aimed at users without heavy math backgrounds. An Introduction to Statistical Learning: with Applications in R
|
4,572 | Book for reading before Elements of Statistical Learning? | I found Programming Collective Intelligence the easiest book for beginners, since the author Toby Segaran is is focused on allowing the median software developer to get his/her hands dirty with data hacking as fast as possible.
Typical chapter: The data problem is clearly described, followed by a rough explanation how... | Book for reading before Elements of Statistical Learning? | I found Programming Collective Intelligence the easiest book for beginners, since the author Toby Segaran is is focused on allowing the median software developer to get his/her hands dirty with data h | Book for reading before Elements of Statistical Learning?
I found Programming Collective Intelligence the easiest book for beginners, since the author Toby Segaran is is focused on allowing the median software developer to get his/her hands dirty with data hacking as fast as possible.
Typical chapter: The data problem... | Book for reading before Elements of Statistical Learning?
I found Programming Collective Intelligence the easiest book for beginners, since the author Toby Segaran is is focused on allowing the median software developer to get his/her hands dirty with data h |
4,573 | Book for reading before Elements of Statistical Learning? | Introduction to Machine Learning, by E. Alpaydin (MIT Press, 2010, 2nd ed.), covers a lot of topics with nice illustrations (much like Bishop's Pattern Recognition and Machine Learning).
In addition, Andrew W. Moore has some nice tutorials on Statistical Data Mining. | Book for reading before Elements of Statistical Learning? | Introduction to Machine Learning, by E. Alpaydin (MIT Press, 2010, 2nd ed.), covers a lot of topics with nice illustrations (much like Bishop's Pattern Recognition and Machine Learning).
In addition, | Book for reading before Elements of Statistical Learning?
Introduction to Machine Learning, by E. Alpaydin (MIT Press, 2010, 2nd ed.), covers a lot of topics with nice illustrations (much like Bishop's Pattern Recognition and Machine Learning).
In addition, Andrew W. Moore has some nice tutorials on Statistical Data M... | Book for reading before Elements of Statistical Learning?
Introduction to Machine Learning, by E. Alpaydin (MIT Press, 2010, 2nd ed.), covers a lot of topics with nice illustrations (much like Bishop's Pattern Recognition and Machine Learning).
In addition, |
4,574 | Book for reading before Elements of Statistical Learning? | Mayhaps Wasserman's All of Statistics would be of interest. You can sample the book from the link given - and just the first few paragraphs of the preface make a hard sale to your market - and you can likely download the book free through Springer if you are associated with a university.
EDIT: Oops, didn't notice how a... | Book for reading before Elements of Statistical Learning? | Mayhaps Wasserman's All of Statistics would be of interest. You can sample the book from the link given - and just the first few paragraphs of the preface make a hard sale to your market - and you can | Book for reading before Elements of Statistical Learning?
Mayhaps Wasserman's All of Statistics would be of interest. You can sample the book from the link given - and just the first few paragraphs of the preface make a hard sale to your market - and you can likely download the book free through Springer if you are ass... | Book for reading before Elements of Statistical Learning?
Mayhaps Wasserman's All of Statistics would be of interest. You can sample the book from the link given - and just the first few paragraphs of the preface make a hard sale to your market - and you can |
4,575 | Book for reading before Elements of Statistical Learning? | The Elements Of Statistical Learning might be a tough read, especially for a self-learner. While searching for some explanations on the second chapter I have stumbled on the following resource: https://waxworksmath.com/Authors/G_M/Hastie/WriteUp/Weatherwax_Epstein_Hastie_Solution_Manual.pdf. It contains 100+ pages of a... | Book for reading before Elements of Statistical Learning? | The Elements Of Statistical Learning might be a tough read, especially for a self-learner. While searching for some explanations on the second chapter I have stumbled on the following resource: https: | Book for reading before Elements of Statistical Learning?
The Elements Of Statistical Learning might be a tough read, especially for a self-learner. While searching for some explanations on the second chapter I have stumbled on the following resource: https://waxworksmath.com/Authors/G_M/Hastie/WriteUp/Weatherwax_Epste... | Book for reading before Elements of Statistical Learning?
The Elements Of Statistical Learning might be a tough read, especially for a self-learner. While searching for some explanations on the second chapter I have stumbled on the following resource: https: |
4,576 | Book for reading before Elements of Statistical Learning? | I'd strongly recommend A First Course in Machine Learning by Rogers and Girolami. It covers the key ideas in a very logical order, with good examples and with the minimum level of maths to have a proper grounding in the fundamentals. It doesn't have the breadth of coverage of some books, but that is exactly why it is... | Book for reading before Elements of Statistical Learning? | I'd strongly recommend A First Course in Machine Learning by Rogers and Girolami. It covers the key ideas in a very logical order, with good examples and with the minimum level of maths to have a pro | Book for reading before Elements of Statistical Learning?
I'd strongly recommend A First Course in Machine Learning by Rogers and Girolami. It covers the key ideas in a very logical order, with good examples and with the minimum level of maths to have a proper grounding in the fundamentals. It doesn't have the breadt... | Book for reading before Elements of Statistical Learning?
I'd strongly recommend A First Course in Machine Learning by Rogers and Girolami. It covers the key ideas in a very logical order, with good examples and with the minimum level of maths to have a pro |
4,577 | Book for reading before Elements of Statistical Learning? | Another book that is very interesting is Bayesian Reasoning and Machine Learning by David Barber. The book is available as a free download from the author's website:
http://www.cs.ucl.ac.uk/staff/d.barber/brml/ | Book for reading before Elements of Statistical Learning? | Another book that is very interesting is Bayesian Reasoning and Machine Learning by David Barber. The book is available as a free download from the author's website:
http://www.cs.ucl.ac.uk/staff/d.b | Book for reading before Elements of Statistical Learning?
Another book that is very interesting is Bayesian Reasoning and Machine Learning by David Barber. The book is available as a free download from the author's website:
http://www.cs.ucl.ac.uk/staff/d.barber/brml/ | Book for reading before Elements of Statistical Learning?
Another book that is very interesting is Bayesian Reasoning and Machine Learning by David Barber. The book is available as a free download from the author's website:
http://www.cs.ucl.ac.uk/staff/d.b |
4,578 | Replicating Stata's "robust" option in R | Charles is nearly there in his answer, but robust option of the regress command (and other regression estimation commands) in Stata makes it possible to use multiple types of heteroskedasticity and autocorrelation robust variance-covariance matrix estimators, as does the coeftest function in the lmtest package, which i... | Replicating Stata's "robust" option in R | Charles is nearly there in his answer, but robust option of the regress command (and other regression estimation commands) in Stata makes it possible to use multiple types of heteroskedasticity and au | Replicating Stata's "robust" option in R
Charles is nearly there in his answer, but robust option of the regress command (and other regression estimation commands) in Stata makes it possible to use multiple types of heteroskedasticity and autocorrelation robust variance-covariance matrix estimators, as does the coeftes... | Replicating Stata's "robust" option in R
Charles is nearly there in his answer, but robust option of the regress command (and other regression estimation commands) in Stata makes it possible to use multiple types of heteroskedasticity and au |
4,579 | Replicating Stata's "robust" option in R | I found a description on the following website that replicates Stata's ''robust'' option in R.
https://economictheoryblog.com/2016/08/08/robust-standard-errors-in-r
Following the instructions, all you need to do is load a function into your R session and then set the parameter ''robust'' in you summary function to TRU... | Replicating Stata's "robust" option in R | I found a description on the following website that replicates Stata's ''robust'' option in R.
https://economictheoryblog.com/2016/08/08/robust-standard-errors-in-r
Following the instructions, all yo | Replicating Stata's "robust" option in R
I found a description on the following website that replicates Stata's ''robust'' option in R.
https://economictheoryblog.com/2016/08/08/robust-standard-errors-in-r
Following the instructions, all you need to do is load a function into your R session and then set the parameter ... | Replicating Stata's "robust" option in R
I found a description on the following website that replicates Stata's ''robust'' option in R.
https://economictheoryblog.com/2016/08/08/robust-standard-errors-in-r
Following the instructions, all yo |
4,580 | Replicating Stata's "robust" option in R | As of April 2018 I believe you want the estimatr package, which provides a near drop in replacement for lm. Several examples pulled nearly from the documentation:
library(estimatr)
library(car)
# HC1 robust standard errors
model <- lm_robust(GPA_year2 ~ gpa0 + ssp, data = alo_star_men,
se_type = "st... | Replicating Stata's "robust" option in R | As of April 2018 I believe you want the estimatr package, which provides a near drop in replacement for lm. Several examples pulled nearly from the documentation:
library(estimatr)
library(car)
# HC1 | Replicating Stata's "robust" option in R
As of April 2018 I believe you want the estimatr package, which provides a near drop in replacement for lm. Several examples pulled nearly from the documentation:
library(estimatr)
library(car)
# HC1 robust standard errors
model <- lm_robust(GPA_year2 ~ gpa0 + ssp, data = alo_s... | Replicating Stata's "robust" option in R
As of April 2018 I believe you want the estimatr package, which provides a near drop in replacement for lm. Several examples pulled nearly from the documentation:
library(estimatr)
library(car)
# HC1 |
4,581 | Replicating Stata's "robust" option in R | I'd edit the question. You're confusing robust regression with Stata's robust command. There seems to be no benefit to introducing this confusion.
I think there are a few approaches. I haven't looked at them all and not sure which is the best:
The sandwich package:
library(sandwich)
coeftest(model, vcov=sandwi... | Replicating Stata's "robust" option in R | I'd edit the question. You're confusing robust regression with Stata's robust command. There seems to be no benefit to introducing this confusion.
I think there are a few approaches. I haven't looked | Replicating Stata's "robust" option in R
I'd edit the question. You're confusing robust regression with Stata's robust command. There seems to be no benefit to introducing this confusion.
I think there are a few approaches. I haven't looked at them all and not sure which is the best:
The sandwich package:
library(... | Replicating Stata's "robust" option in R
I'd edit the question. You're confusing robust regression with Stata's robust command. There seems to be no benefit to introducing this confusion.
I think there are a few approaches. I haven't looked |
4,582 | Generic sum of Gamma random variables | First, combine any sums having the same scale factor: a $\Gamma(n, \beta)$ plus a $\Gamma(m,\beta)$ variate form a $\Gamma(n+m,\beta)$ variate.
Next, observe that the characteristic function (cf) of $\Gamma(n, \beta)$ is $(1-i \beta t)^{-n}$, whence the cf of a sum of these distributions is the product
$$\prod_{j} \fr... | Generic sum of Gamma random variables | First, combine any sums having the same scale factor: a $\Gamma(n, \beta)$ plus a $\Gamma(m,\beta)$ variate form a $\Gamma(n+m,\beta)$ variate.
Next, observe that the characteristic function (cf) of $ | Generic sum of Gamma random variables
First, combine any sums having the same scale factor: a $\Gamma(n, \beta)$ plus a $\Gamma(m,\beta)$ variate form a $\Gamma(n+m,\beta)$ variate.
Next, observe that the characteristic function (cf) of $\Gamma(n, \beta)$ is $(1-i \beta t)^{-n}$, whence the cf of a sum of these distri... | Generic sum of Gamma random variables
First, combine any sums having the same scale factor: a $\Gamma(n, \beta)$ plus a $\Gamma(m,\beta)$ variate form a $\Gamma(n+m,\beta)$ variate.
Next, observe that the characteristic function (cf) of $ |
4,583 | Generic sum of Gamma random variables | The Welch–Satterthwaite equation could be used to give an approximate answer in the form of a gamma distribution. This has the nice property of letting us treat gamma distributions as being (approximately) closed under addition. This is the approximation in the commonly used Welch's t-test.
(The gamma distribution is c... | Generic sum of Gamma random variables | The Welch–Satterthwaite equation could be used to give an approximate answer in the form of a gamma distribution. This has the nice property of letting us treat gamma distributions as being (approxima | Generic sum of Gamma random variables
The Welch–Satterthwaite equation could be used to give an approximate answer in the form of a gamma distribution. This has the nice property of letting us treat gamma distributions as being (approximately) closed under addition. This is the approximation in the commonly used Welch'... | Generic sum of Gamma random variables
The Welch–Satterthwaite equation could be used to give an approximate answer in the form of a gamma distribution. This has the nice property of letting us treat gamma distributions as being (approxima |
4,584 | Generic sum of Gamma random variables | I will show another possible solution, that is quite widely applicable, and with todays R software, quite easy to implement. That is the saddlepoint density approximation, which ought to be wider known!
For terminology about the gamma distribution, I will follow https://en.wikipedia.org/wiki/Gamma_distribution with t... | Generic sum of Gamma random variables | I will show another possible solution, that is quite widely applicable, and with todays R software, quite easy to implement. That is the saddlepoint density approximation, which ought to be wider kno | Generic sum of Gamma random variables
I will show another possible solution, that is quite widely applicable, and with todays R software, quite easy to implement. That is the saddlepoint density approximation, which ought to be wider known!
For terminology about the gamma distribution, I will follow https://en.wikiped... | Generic sum of Gamma random variables
I will show another possible solution, that is quite widely applicable, and with todays R software, quite easy to implement. That is the saddlepoint density approximation, which ought to be wider kno |
4,585 | Generic sum of Gamma random variables | An exact solution to the convolution (i.e., sum) of $n$ gamma distributions is given as Eq. (1) in the linked pdf by DiSalvo. As this is a bit long, it will take some time to copy it over here. For only two gamma distributions, their exact sum in closed form is specified by Eq. (2) of DiSalvo and without weights by Eq.... | Generic sum of Gamma random variables | An exact solution to the convolution (i.e., sum) of $n$ gamma distributions is given as Eq. (1) in the linked pdf by DiSalvo. As this is a bit long, it will take some time to copy it over here. For on | Generic sum of Gamma random variables
An exact solution to the convolution (i.e., sum) of $n$ gamma distributions is given as Eq. (1) in the linked pdf by DiSalvo. As this is a bit long, it will take some time to copy it over here. For only two gamma distributions, their exact sum in closed form is specified by Eq. (2)... | Generic sum of Gamma random variables
An exact solution to the convolution (i.e., sum) of $n$ gamma distributions is given as Eq. (1) in the linked pdf by DiSalvo. As this is a bit long, it will take some time to copy it over here. For on |
4,586 | Generic sum of Gamma random variables | According to Ansari et al. 2012, the PDF and CDF of independent gamma random variables with different distribution can be expressed in terms of Fox's Ĥ function (H-bar function). The paper referenced below also contains an implementation of this function in the Wolfram language.
REFERENCES:
Ansari, I.S., Yilmaz, F., ... | Generic sum of Gamma random variables | According to Ansari et al. 2012, the PDF and CDF of independent gamma random variables with different distribution can be expressed in terms of Fox's Ĥ function (H-bar function). The paper referenced | Generic sum of Gamma random variables
According to Ansari et al. 2012, the PDF and CDF of independent gamma random variables with different distribution can be expressed in terms of Fox's Ĥ function (H-bar function). The paper referenced below also contains an implementation of this function in the Wolfram language.
R... | Generic sum of Gamma random variables
According to Ansari et al. 2012, the PDF and CDF of independent gamma random variables with different distribution can be expressed in terms of Fox's Ĥ function (H-bar function). The paper referenced |
4,587 | Is sampling relevant in the time of 'big data'? | In a word, yes. I believe there are still clear situations where sampling is appropriate, within and without the "big data" world, but the nature of big data will certainly change our approach to sampling, and we will use more datasets that are nearly complete representations of the underlying population.
On sampling: ... | Is sampling relevant in the time of 'big data'? | In a word, yes. I believe there are still clear situations where sampling is appropriate, within and without the "big data" world, but the nature of big data will certainly change our approach to samp | Is sampling relevant in the time of 'big data'?
In a word, yes. I believe there are still clear situations where sampling is appropriate, within and without the "big data" world, but the nature of big data will certainly change our approach to sampling, and we will use more datasets that are nearly complete representat... | Is sampling relevant in the time of 'big data'?
In a word, yes. I believe there are still clear situations where sampling is appropriate, within and without the "big data" world, but the nature of big data will certainly change our approach to samp |
4,588 | Is sampling relevant in the time of 'big data'? | While there may be hell of a lot of Big Data being produced by the mobile devices and such, there is little usable data in it. If you want to predict the urban travel patterns using foursquare, you may be off by an order of magnitude in estimated flows. Worse, you won't know if you are overestimated or underestimating ... | Is sampling relevant in the time of 'big data'? | While there may be hell of a lot of Big Data being produced by the mobile devices and such, there is little usable data in it. If you want to predict the urban travel patterns using foursquare, you ma | Is sampling relevant in the time of 'big data'?
While there may be hell of a lot of Big Data being produced by the mobile devices and such, there is little usable data in it. If you want to predict the urban travel patterns using foursquare, you may be off by an order of magnitude in estimated flows. Worse, you won't k... | Is sampling relevant in the time of 'big data'?
While there may be hell of a lot of Big Data being produced by the mobile devices and such, there is little usable data in it. If you want to predict the urban travel patterns using foursquare, you ma |
4,589 | Is sampling relevant in the time of 'big data'? | Whenever one applies techniques of statistical inference, it is important to be clear as to the population about which one aims to draw conclusions. Even if the data that has been collected is very big, it may still relate only to a small part of the population, and may not be very representative of the whole.
Suppose... | Is sampling relevant in the time of 'big data'? | Whenever one applies techniques of statistical inference, it is important to be clear as to the population about which one aims to draw conclusions. Even if the data that has been collected is very b | Is sampling relevant in the time of 'big data'?
Whenever one applies techniques of statistical inference, it is important to be clear as to the population about which one aims to draw conclusions. Even if the data that has been collected is very big, it may still relate only to a small part of the population, and may ... | Is sampling relevant in the time of 'big data'?
Whenever one applies techniques of statistical inference, it is important to be clear as to the population about which one aims to draw conclusions. Even if the data that has been collected is very b |
4,590 | Is sampling relevant in the time of 'big data'? | From what I've seen of the big data/ML craze, thinking about sampling and the population from which your sample is drawn is just as important as ever--but thought about even less.
I'm "auditing" the Stanford ML class, and thus far we've covered regression and neural networks with nary a mention of population inference.... | Is sampling relevant in the time of 'big data'? | From what I've seen of the big data/ML craze, thinking about sampling and the population from which your sample is drawn is just as important as ever--but thought about even less.
I'm "auditing" the S | Is sampling relevant in the time of 'big data'?
From what I've seen of the big data/ML craze, thinking about sampling and the population from which your sample is drawn is just as important as ever--but thought about even less.
I'm "auditing" the Stanford ML class, and thus far we've covered regression and neural netwo... | Is sampling relevant in the time of 'big data'?
From what I've seen of the big data/ML craze, thinking about sampling and the population from which your sample is drawn is just as important as ever--but thought about even less.
I'm "auditing" the S |
4,591 | Is sampling relevant in the time of 'big data'? | Yes, sampling is relevant and will remain relevant. Bottom line is that the accuracy of a statistical estimate is generally a function of the sample size, not the population to which we want to generalize. So a mean or an average proportion computed from a sample of 1,000 respondents will yield an estimate of a certain... | Is sampling relevant in the time of 'big data'? | Yes, sampling is relevant and will remain relevant. Bottom line is that the accuracy of a statistical estimate is generally a function of the sample size, not the population to which we want to genera | Is sampling relevant in the time of 'big data'?
Yes, sampling is relevant and will remain relevant. Bottom line is that the accuracy of a statistical estimate is generally a function of the sample size, not the population to which we want to generalize. So a mean or an average proportion computed from a sample of 1,000... | Is sampling relevant in the time of 'big data'?
Yes, sampling is relevant and will remain relevant. Bottom line is that the accuracy of a statistical estimate is generally a function of the sample size, not the population to which we want to genera |
4,592 | Is sampling relevant in the time of 'big data'? | Many big data methods are actually designed around sampling.
The question should be more on the line of:
Shouldn't we use systematic sampling with big data, too?
A lot of the "big data" stuff is still pretty fresh, and sometimes naive. K-means for example can be trivially parallelized, and thus works for "big data" (... | Is sampling relevant in the time of 'big data'? | Many big data methods are actually designed around sampling.
The question should be more on the line of:
Shouldn't we use systematic sampling with big data, too?
A lot of the "big data" stuff is sti | Is sampling relevant in the time of 'big data'?
Many big data methods are actually designed around sampling.
The question should be more on the line of:
Shouldn't we use systematic sampling with big data, too?
A lot of the "big data" stuff is still pretty fresh, and sometimes naive. K-means for example can be trivial... | Is sampling relevant in the time of 'big data'?
Many big data methods are actually designed around sampling.
The question should be more on the line of:
Shouldn't we use systematic sampling with big data, too?
A lot of the "big data" stuff is sti |
4,593 | Is sampling relevant in the time of 'big data'? | Cross validation is an specific example of sub-sampling which is quite important in ML/big data. More generally, big data is still usually a sample of a population, as other people here have mentioned.
But, I think OP might be specifically referring to sampling as it applies to a controlled experiments, versus observat... | Is sampling relevant in the time of 'big data'? | Cross validation is an specific example of sub-sampling which is quite important in ML/big data. More generally, big data is still usually a sample of a population, as other people here have mentioned | Is sampling relevant in the time of 'big data'?
Cross validation is an specific example of sub-sampling which is quite important in ML/big data. More generally, big data is still usually a sample of a population, as other people here have mentioned.
But, I think OP might be specifically referring to sampling as it appl... | Is sampling relevant in the time of 'big data'?
Cross validation is an specific example of sub-sampling which is quite important in ML/big data. More generally, big data is still usually a sample of a population, as other people here have mentioned |
4,594 | Is sampling relevant in the time of 'big data'? | In the areas where Big Data is gaining popularity: Search, Advertising, Recommender Systems like Amazon, Netflix , there is a very Big incentive to explore the entire data set.
The objective of these systems is to tailor recommendations / suggestions to every single member of the population. Also, the number of attrib... | Is sampling relevant in the time of 'big data'? | In the areas where Big Data is gaining popularity: Search, Advertising, Recommender Systems like Amazon, Netflix , there is a very Big incentive to explore the entire data set.
The objective of these | Is sampling relevant in the time of 'big data'?
In the areas where Big Data is gaining popularity: Search, Advertising, Recommender Systems like Amazon, Netflix , there is a very Big incentive to explore the entire data set.
The objective of these systems is to tailor recommendations / suggestions to every single memb... | Is sampling relevant in the time of 'big data'?
In the areas where Big Data is gaining popularity: Search, Advertising, Recommender Systems like Amazon, Netflix , there is a very Big incentive to explore the entire data set.
The objective of these |
4,595 | Choosing the right linkage method for hierarchical clustering | Methods overview
Short reference about some linkage methods of hierarchical agglomerative cluster analysis (HAC).
Basic version of HAC algorithm is one generic; it amounts to updating, at each step, by the formula known as Lance-Williams formula, the proximities between the emergent (merged of two) cluster and all the ... | Choosing the right linkage method for hierarchical clustering | Methods overview
Short reference about some linkage methods of hierarchical agglomerative cluster analysis (HAC).
Basic version of HAC algorithm is one generic; it amounts to updating, at each step, b | Choosing the right linkage method for hierarchical clustering
Methods overview
Short reference about some linkage methods of hierarchical agglomerative cluster analysis (HAC).
Basic version of HAC algorithm is one generic; it amounts to updating, at each step, by the formula known as Lance-Williams formula, the proximi... | Choosing the right linkage method for hierarchical clustering
Methods overview
Short reference about some linkage methods of hierarchical agglomerative cluster analysis (HAC).
Basic version of HAC algorithm is one generic; it amounts to updating, at each step, b |
4,596 | Choosing the right linkage method for hierarchical clustering | The correlation between the distance matrix and the cophenetic distance is one metric to help assess which clustering linkage to select. From ?cophenetic:
It can be argued that a dendrogram is an appropriate summary of some
data if the correlation between the original distances and the
cophenetic distances is high... | Choosing the right linkage method for hierarchical clustering | The correlation between the distance matrix and the cophenetic distance is one metric to help assess which clustering linkage to select. From ?cophenetic:
It can be argued that a dendrogram is an app | Choosing the right linkage method for hierarchical clustering
The correlation between the distance matrix and the cophenetic distance is one metric to help assess which clustering linkage to select. From ?cophenetic:
It can be argued that a dendrogram is an appropriate summary of some
data if the correlation between... | Choosing the right linkage method for hierarchical clustering
The correlation between the distance matrix and the cophenetic distance is one metric to help assess which clustering linkage to select. From ?cophenetic:
It can be argued that a dendrogram is an app |
4,597 | CNN architectures for regression? | First of all a general suggestion: do a literature search before you start making experiments on a topic you're not familiar with. You'll save yourself a lot of time.
In this case, looking at existing papers you may have noticed that
CNNs have been used multiple times for regression: this is a classic but it's old (... | CNN architectures for regression? | First of all a general suggestion: do a literature search before you start making experiments on a topic you're not familiar with. You'll save yourself a lot of time.
In this case, looking at existin | CNN architectures for regression?
First of all a general suggestion: do a literature search before you start making experiments on a topic you're not familiar with. You'll save yourself a lot of time.
In this case, looking at existing papers you may have noticed that
CNNs have been used multiple times for regression... | CNN architectures for regression?
First of all a general suggestion: do a literature search before you start making experiments on a topic you're not familiar with. You'll save yourself a lot of time.
In this case, looking at existin |
4,598 | Why does frequentist hypothesis testing become biased towards rejecting the null hypothesis with sufficiently large samples? | Answer to question 1: This occurs because the $p$-value becomes arbitrarily small as the sample size increases in frequentist tests for difference (i.e. tests with a null hypothesis of no difference/some form of equality) when a true difference exactly equal to zero, as opposed to arbitraily close to zero, is not reali... | Why does frequentist hypothesis testing become biased towards rejecting the null hypothesis with suf | Answer to question 1: This occurs because the $p$-value becomes arbitrarily small as the sample size increases in frequentist tests for difference (i.e. tests with a null hypothesis of no difference/s | Why does frequentist hypothesis testing become biased towards rejecting the null hypothesis with sufficiently large samples?
Answer to question 1: This occurs because the $p$-value becomes arbitrarily small as the sample size increases in frequentist tests for difference (i.e. tests with a null hypothesis of no differe... | Why does frequentist hypothesis testing become biased towards rejecting the null hypothesis with suf
Answer to question 1: This occurs because the $p$-value becomes arbitrarily small as the sample size increases in frequentist tests for difference (i.e. tests with a null hypothesis of no difference/s |
4,599 | Why does frequentist hypothesis testing become biased towards rejecting the null hypothesis with sufficiently large samples? | Frequentist tests with large samples DO NOT exhibit bias towards rejecting the null hypothesis if the null hypothesis is true. If the assumptions of the test are valid and the null hypothesis is true then there is no more risk of a large sample leading to rejection of the null hypothesis than a small sample. If the nul... | Why does frequentist hypothesis testing become biased towards rejecting the null hypothesis with suf | Frequentist tests with large samples DO NOT exhibit bias towards rejecting the null hypothesis if the null hypothesis is true. If the assumptions of the test are valid and the null hypothesis is true | Why does frequentist hypothesis testing become biased towards rejecting the null hypothesis with sufficiently large samples?
Frequentist tests with large samples DO NOT exhibit bias towards rejecting the null hypothesis if the null hypothesis is true. If the assumptions of the test are valid and the null hypothesis is ... | Why does frequentist hypothesis testing become biased towards rejecting the null hypothesis with suf
Frequentist tests with large samples DO NOT exhibit bias towards rejecting the null hypothesis if the null hypothesis is true. If the assumptions of the test are valid and the null hypothesis is true |
4,600 | Why does frequentist hypothesis testing become biased towards rejecting the null hypothesis with sufficiently large samples? | Nobody seems to have noted that with good experimental design, sample sizes should be chosen to reflect a meaningful difference under H1 (ie as large as needed but no larger). The problem of rejecting H0 because of a huge sample and a trivial difference is thus avoided. | Why does frequentist hypothesis testing become biased towards rejecting the null hypothesis with suf | Nobody seems to have noted that with good experimental design, sample sizes should be chosen to reflect a meaningful difference under H1 (ie as large as needed but no larger). The problem of rejectin | Why does frequentist hypothesis testing become biased towards rejecting the null hypothesis with sufficiently large samples?
Nobody seems to have noted that with good experimental design, sample sizes should be chosen to reflect a meaningful difference under H1 (ie as large as needed but no larger). The problem of rej... | Why does frequentist hypothesis testing become biased towards rejecting the null hypothesis with suf
Nobody seems to have noted that with good experimental design, sample sizes should be chosen to reflect a meaningful difference under H1 (ie as large as needed but no larger). The problem of rejectin |
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