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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15,701 | Why does the supremum of the Brownian bridge have the Kolmogorov–Smirnov distribution? | For Kolmogorov-Smirnov, consider the null hypothesis. It says that a sample is drawn from a particular distribution. So if you construct the empirical distribution function for $n$ samples $f(x) = \frac{1}{n} \sum_i \chi_{(-\infty, X_i]}(x)$, in the limit of infinite data, it will converge to the underlying distribut... | Why does the supremum of the Brownian bridge have the Kolmogorov–Smirnov distribution? | For Kolmogorov-Smirnov, consider the null hypothesis. It says that a sample is drawn from a particular distribution. So if you construct the empirical distribution function for $n$ samples $f(x) = \ | Why does the supremum of the Brownian bridge have the Kolmogorov–Smirnov distribution?
For Kolmogorov-Smirnov, consider the null hypothesis. It says that a sample is drawn from a particular distribution. So if you construct the empirical distribution function for $n$ samples $f(x) = \frac{1}{n} \sum_i \chi_{(-\infty,... | Why does the supremum of the Brownian bridge have the Kolmogorov–Smirnov distribution?
For Kolmogorov-Smirnov, consider the null hypothesis. It says that a sample is drawn from a particular distribution. So if you construct the empirical distribution function for $n$ samples $f(x) = \ |
15,702 | Is it 'fair' to set a seed in a random forest regression to yield the highest accuracy? | The answer is no.
Your model gives a different result for each seed you use. This is a result of the non-deterministic nature of the model. By choosing a specific seed that maximizes the performance on the validation set means that you chose the "arrangement" that best fits this set. However, this does not guarantee th... | Is it 'fair' to set a seed in a random forest regression to yield the highest accuracy? | The answer is no.
Your model gives a different result for each seed you use. This is a result of the non-deterministic nature of the model. By choosing a specific seed that maximizes the performance o | Is it 'fair' to set a seed in a random forest regression to yield the highest accuracy?
The answer is no.
Your model gives a different result for each seed you use. This is a result of the non-deterministic nature of the model. By choosing a specific seed that maximizes the performance on the validation set means that ... | Is it 'fair' to set a seed in a random forest regression to yield the highest accuracy?
The answer is no.
Your model gives a different result for each seed you use. This is a result of the non-deterministic nature of the model. By choosing a specific seed that maximizes the performance o |
15,703 | Is it 'fair' to set a seed in a random forest regression to yield the highest accuracy? | For some algorithms a bad initialization may matter and may be due to the particular random seed. In such cases, it may make sense to try to find a good initialitzation (=good random seed) that then leads to a good performance (or to find a way of modifying the training to reduce such effects). However, one should real... | Is it 'fair' to set a seed in a random forest regression to yield the highest accuracy? | For some algorithms a bad initialization may matter and may be due to the particular random seed. In such cases, it may make sense to try to find a good initialitzation (=good random seed) that then l | Is it 'fair' to set a seed in a random forest regression to yield the highest accuracy?
For some algorithms a bad initialization may matter and may be due to the particular random seed. In such cases, it may make sense to try to find a good initialitzation (=good random seed) that then leads to a good performance (or t... | Is it 'fair' to set a seed in a random forest regression to yield the highest accuracy?
For some algorithms a bad initialization may matter and may be due to the particular random seed. In such cases, it may make sense to try to find a good initialitzation (=good random seed) that then l |
15,704 | Is it 'fair' to set a seed in a random forest regression to yield the highest accuracy? | The short answer is YES, it is both fair and correct, contrary to what @Djib2011 wrote in a separate answer.
If you follow the usual procedure in ML, then setting the seed in this context does NOT lead to overfitting, contrary to the other answer here is falsely suggesting. You can call it "seed optimization" or "seed ... | Is it 'fair' to set a seed in a random forest regression to yield the highest accuracy? | The short answer is YES, it is both fair and correct, contrary to what @Djib2011 wrote in a separate answer.
If you follow the usual procedure in ML, then setting the seed in this context does NOT lea | Is it 'fair' to set a seed in a random forest regression to yield the highest accuracy?
The short answer is YES, it is both fair and correct, contrary to what @Djib2011 wrote in a separate answer.
If you follow the usual procedure in ML, then setting the seed in this context does NOT lead to overfitting, contrary to th... | Is it 'fair' to set a seed in a random forest regression to yield the highest accuracy?
The short answer is YES, it is both fair and correct, contrary to what @Djib2011 wrote in a separate answer.
If you follow the usual procedure in ML, then setting the seed in this context does NOT lea |
15,705 | Feature scaling and mean normalization [closed] | $x^{(4)}_2 \to 4761$.
Nomalized feature $\to \dfrac{x - u}{s}$ where $u$ is average of $X$ and $s = max - min = 8836 - 4761 = 4075$.
Finally, $\dfrac{4761 - 6675.5}{4075} = -0.47$ | Feature scaling and mean normalization [closed] | $x^{(4)}_2 \to 4761$.
Nomalized feature $\to \dfrac{x - u}{s}$ where $u$ is average of $X$ and $s = max - min = 8836 - 4761 = 4075$.
Finally, $\dfrac{4761 - 6675.5}{4075} = -0.47$ | Feature scaling and mean normalization [closed]
$x^{(4)}_2 \to 4761$.
Nomalized feature $\to \dfrac{x - u}{s}$ where $u$ is average of $X$ and $s = max - min = 8836 - 4761 = 4075$.
Finally, $\dfrac{4761 - 6675.5}{4075} = -0.47$ | Feature scaling and mean normalization [closed]
$x^{(4)}_2 \to 4761$.
Nomalized feature $\to \dfrac{x - u}{s}$ where $u$ is average of $X$ and $s = max - min = 8836 - 4761 = 4075$.
Finally, $\dfrac{4761 - 6675.5}{4075} = -0.47$ |
15,706 | Feature scaling and mean normalization [closed] | My answer:
Average = (7921 + 5184 + 8836 + 4761)/4
= 6675.5
Range = 8836 - 4761
= 4075
x2 = (5184 - 6675.5)/4075
= -0.366
= -0.37 (rounded to 2 decimal places)
Edited:
I got the error. I should have rounded to 2 decimal places. | Feature scaling and mean normalization [closed] | My answer:
Average = (7921 + 5184 + 8836 + 4761)/4
= 6675.5
Range = 8836 - 4761
= 4075
x2 = (5184 - 6675.5)/4075
= -0.366
= -0.37 (rounded to 2 decimal places)
Edited | Feature scaling and mean normalization [closed]
My answer:
Average = (7921 + 5184 + 8836 + 4761)/4
= 6675.5
Range = 8836 - 4761
= 4075
x2 = (5184 - 6675.5)/4075
= -0.366
= -0.37 (rounded to 2 decimal places)
Edited:
I got the error. I should have rounded to 2 decimal places. | Feature scaling and mean normalization [closed]
My answer:
Average = (7921 + 5184 + 8836 + 4761)/4
= 6675.5
Range = 8836 - 4761
= 4075
x2 = (5184 - 6675.5)/4075
= -0.366
= -0.37 (rounded to 2 decimal places)
Edited |
15,707 | Feature scaling and mean normalization [closed] | Since , normalized $x = \frac{x - u}{s}$
where
u = mean of the feature x,
s = $range(max - min)$ or standard deviation
Here, in this quiz s means the range actually so,
normalized x = $\frac{4761 - 6675.5}{8836 - 4761}$ = -0.47 | Feature scaling and mean normalization [closed] | Since , normalized $x = \frac{x - u}{s}$
where
u = mean of the feature x,
s = $range(max - min)$ or standard deviation
Here, in this quiz s means the range actually so,
normalized x = $\frac | Feature scaling and mean normalization [closed]
Since , normalized $x = \frac{x - u}{s}$
where
u = mean of the feature x,
s = $range(max - min)$ or standard deviation
Here, in this quiz s means the range actually so,
normalized x = $\frac{4761 - 6675.5}{8836 - 4761}$ = -0.47 | Feature scaling and mean normalization [closed]
Since , normalized $x = \frac{x - u}{s}$
where
u = mean of the feature x,
s = $range(max - min)$ or standard deviation
Here, in this quiz s means the range actually so,
normalized x = $\frac |
15,708 | Feature scaling and mean normalization [closed] | Read the guide please :
They said : Please round off your answer to two decimal places and enter in the text box below.
The answer is -0.37 . I did it and success. | Feature scaling and mean normalization [closed] | Read the guide please :
They said : Please round off your answer to two decimal places and enter in the text box below.
The answer is -0.37 . I did it and success. | Feature scaling and mean normalization [closed]
Read the guide please :
They said : Please round off your answer to two decimal places and enter in the text box below.
The answer is -0.37 . I did it and success. | Feature scaling and mean normalization [closed]
Read the guide please :
They said : Please round off your answer to two decimal places and enter in the text box below.
The answer is -0.37 . I did it and success. |
15,709 | Feature scaling and mean normalization [closed] | x(4)2→4761.
Nomalized feature →x−us where u is average of X and s=max−min=8836−4761=4075.
Finally, 4761−6675.54075 = −0.47
So Answer is : −0.47 | Feature scaling and mean normalization [closed] | x(4)2→4761.
Nomalized feature →x−us where u is average of X and s=max−min=8836−4761=4075.
Finally, 4761−6675.54075 = −0.47
So Answer is : −0.47 | Feature scaling and mean normalization [closed]
x(4)2→4761.
Nomalized feature →x−us where u is average of X and s=max−min=8836−4761=4075.
Finally, 4761−6675.54075 = −0.47
So Answer is : −0.47 | Feature scaling and mean normalization [closed]
x(4)2→4761.
Nomalized feature →x−us where u is average of X and s=max−min=8836−4761=4075.
Finally, 4761−6675.54075 = −0.47
So Answer is : −0.47 |
15,710 | Confusion with Augmented Dickey Fuller test | Since you take the default value of k in adf.test, which in this case is 7, you're basically testing if the information set of the past 7 months helps explain $x_t - x_{t-1}$. Electricity usage has strong seasonality, as your plot shows, and is likely to be cyclical beyond a 7-month period. If you set k=12 and retest, ... | Confusion with Augmented Dickey Fuller test | Since you take the default value of k in adf.test, which in this case is 7, you're basically testing if the information set of the past 7 months helps explain $x_t - x_{t-1}$. Electricity usage has st | Confusion with Augmented Dickey Fuller test
Since you take the default value of k in adf.test, which in this case is 7, you're basically testing if the information set of the past 7 months helps explain $x_t - x_{t-1}$. Electricity usage has strong seasonality, as your plot shows, and is likely to be cyclical beyond a ... | Confusion with Augmented Dickey Fuller test
Since you take the default value of k in adf.test, which in this case is 7, you're basically testing if the information set of the past 7 months helps explain $x_t - x_{t-1}$. Electricity usage has st |
15,711 | Confusion with Augmented Dickey Fuller test | Assuming that "adf.test" really comes from the "tseries" package (directly or indirectly), the reason would be that it automatically includes a linear time trend. From the tseries doc (version 0.10-35): "The general regression equation which incorporates a constant and a linear trend is used [...]" So the test result i... | Confusion with Augmented Dickey Fuller test | Assuming that "adf.test" really comes from the "tseries" package (directly or indirectly), the reason would be that it automatically includes a linear time trend. From the tseries doc (version 0.10-35 | Confusion with Augmented Dickey Fuller test
Assuming that "adf.test" really comes from the "tseries" package (directly or indirectly), the reason would be that it automatically includes a linear time trend. From the tseries doc (version 0.10-35): "The general regression equation which incorporates a constant and a line... | Confusion with Augmented Dickey Fuller test
Assuming that "adf.test" really comes from the "tseries" package (directly or indirectly), the reason would be that it automatically includes a linear time trend. From the tseries doc (version 0.10-35 |
15,712 | Can I use glm algorithms to do a multinomial logistic regression? | Yes, with a Poisson GLM (log linear model) you can fit multinomial models. Hence multinomial logistic or log linear Poisson models are equivalent.
You need to see random counts $y_{ij}$ as Poisson random variables with means $μ_{ij}$ and specify the following the following log-linear model
$\log(μ_{ij}) = o + p_i + c_... | Can I use glm algorithms to do a multinomial logistic regression? | Yes, with a Poisson GLM (log linear model) you can fit multinomial models. Hence multinomial logistic or log linear Poisson models are equivalent.
You need to see random counts $y_{ij}$ as Poisson ra | Can I use glm algorithms to do a multinomial logistic regression?
Yes, with a Poisson GLM (log linear model) you can fit multinomial models. Hence multinomial logistic or log linear Poisson models are equivalent.
You need to see random counts $y_{ij}$ as Poisson random variables with means $μ_{ij}$ and specify the fol... | Can I use glm algorithms to do a multinomial logistic regression?
Yes, with a Poisson GLM (log linear model) you can fit multinomial models. Hence multinomial logistic or log linear Poisson models are equivalent.
You need to see random counts $y_{ij}$ as Poisson ra |
15,713 | Can I use glm algorithms to do a multinomial logistic regression? | Yes you can, and in fact this is precisely what the R package GLMNET does for multinomial logistic regression. Writing the log-likelihood function as:
$$LogL=\sum_i\sum_cn_{ic}\log(p_{ic})$$
Where $i$ denotes observations and $c$ denotes the multinomial categories $n_{ic}$ is the observed count for observation $i$ in ... | Can I use glm algorithms to do a multinomial logistic regression? | Yes you can, and in fact this is precisely what the R package GLMNET does for multinomial logistic regression. Writing the log-likelihood function as:
$$LogL=\sum_i\sum_cn_{ic}\log(p_{ic})$$
Where $i | Can I use glm algorithms to do a multinomial logistic regression?
Yes you can, and in fact this is precisely what the R package GLMNET does for multinomial logistic regression. Writing the log-likelihood function as:
$$LogL=\sum_i\sum_cn_{ic}\log(p_{ic})$$
Where $i$ denotes observations and $c$ denotes the multinomial... | Can I use glm algorithms to do a multinomial logistic regression?
Yes you can, and in fact this is precisely what the R package GLMNET does for multinomial logistic regression. Writing the log-likelihood function as:
$$LogL=\sum_i\sum_cn_{ic}\log(p_{ic})$$
Where $i |
15,714 | On George Box, Galit Shmueli and the scientific method? | Let me start with the pithy quote by George Box, that "all models are wrong, but some are useful". This statement is an encapsulation of the methodological approach of "positivism", which is a philosophical approach that is highly influential in the sciences. This approach is described in detail (in the context of ec... | On George Box, Galit Shmueli and the scientific method? | Let me start with the pithy quote by George Box, that "all models are wrong, but some are useful". This statement is an encapsulation of the methodological approach of "positivism", which is a philos | On George Box, Galit Shmueli and the scientific method?
Let me start with the pithy quote by George Box, that "all models are wrong, but some are useful". This statement is an encapsulation of the methodological approach of "positivism", which is a philosophical approach that is highly influential in the sciences. Th... | On George Box, Galit Shmueli and the scientific method?
Let me start with the pithy quote by George Box, that "all models are wrong, but some are useful". This statement is an encapsulation of the methodological approach of "positivism", which is a philos |
15,715 | On George Box, Galit Shmueli and the scientific method? | A model, when used to explain things, is a simplification of reality. Simplification is just another word for "wrong in some useful way". For example, if we round the number 3.1415926535898 to 3.14 we are making an error, but this error allows us humans to focus on the most important part of that number. This is how m... | On George Box, Galit Shmueli and the scientific method? | A model, when used to explain things, is a simplification of reality. Simplification is just another word for "wrong in some useful way". For example, if we round the number 3.1415926535898 to 3.14 w | On George Box, Galit Shmueli and the scientific method?
A model, when used to explain things, is a simplification of reality. Simplification is just another word for "wrong in some useful way". For example, if we round the number 3.1415926535898 to 3.14 we are making an error, but this error allows us humans to focus ... | On George Box, Galit Shmueli and the scientific method?
A model, when used to explain things, is a simplification of reality. Simplification is just another word for "wrong in some useful way". For example, if we round the number 3.1415926535898 to 3.14 w |
15,716 | On George Box, Galit Shmueli and the scientific method? | An example of a model that is excellent at prediction but does not explain anything is given in the Wikipedia article “All models are wrong”. The example is Newton’s model of gravitation. Newton’s model almost always gives predictions that are indistinguishable from empirical observations. Yet the model is extremely im... | On George Box, Galit Shmueli and the scientific method? | An example of a model that is excellent at prediction but does not explain anything is given in the Wikipedia article “All models are wrong”. The example is Newton’s model of gravitation. Newton’s mod | On George Box, Galit Shmueli and the scientific method?
An example of a model that is excellent at prediction but does not explain anything is given in the Wikipedia article “All models are wrong”. The example is Newton’s model of gravitation. Newton’s model almost always gives predictions that are indistinguishable fr... | On George Box, Galit Shmueli and the scientific method?
An example of a model that is excellent at prediction but does not explain anything is given in the Wikipedia article “All models are wrong”. The example is Newton’s model of gravitation. Newton’s mod |
15,717 | On George Box, Galit Shmueli and the scientific method? | I'm an undergraduate in Statistics, so I won't call myself an expert, but here are my two cents.
Models don't explain themselves; humans interpret them. Linear models are easier to understand than neural networks and random forests because they are closer to how we make decisions. Indeed, ANNs imitate the human brain, ... | On George Box, Galit Shmueli and the scientific method? | I'm an undergraduate in Statistics, so I won't call myself an expert, but here are my two cents.
Models don't explain themselves; humans interpret them. Linear models are easier to understand than neu | On George Box, Galit Shmueli and the scientific method?
I'm an undergraduate in Statistics, so I won't call myself an expert, but here are my two cents.
Models don't explain themselves; humans interpret them. Linear models are easier to understand than neural networks and random forests because they are closer to how w... | On George Box, Galit Shmueli and the scientific method?
I'm an undergraduate in Statistics, so I won't call myself an expert, but here are my two cents.
Models don't explain themselves; humans interpret them. Linear models are easier to understand than neu |
15,718 | Boosting A Logistic Regression Model | Don't confuse the handling of the predictors (via base learners, e.g. stumps) and the handling of the loss function in boosting. Although AdaBoost can be thought of as finding combinations of base learners to minimize misclassification error, the "Additive Logistic Regression" paper you cite shows that it can also be f... | Boosting A Logistic Regression Model | Don't confuse the handling of the predictors (via base learners, e.g. stumps) and the handling of the loss function in boosting. Although AdaBoost can be thought of as finding combinations of base lea | Boosting A Logistic Regression Model
Don't confuse the handling of the predictors (via base learners, e.g. stumps) and the handling of the loss function in boosting. Although AdaBoost can be thought of as finding combinations of base learners to minimize misclassification error, the "Additive Logistic Regression" paper... | Boosting A Logistic Regression Model
Don't confuse the handling of the predictors (via base learners, e.g. stumps) and the handling of the loss function in boosting. Although AdaBoost can be thought of as finding combinations of base lea |
15,719 | Boosting A Logistic Regression Model | In fact we have a very similar question here on regression case. And we had a very good answer by @Matthew Drury
Gradient Boosting for Linear Regression - why does it not work?
Linear model (such as logistic regression) is not good for boosting. The reason is if you add two linear models together, the result is another... | Boosting A Logistic Regression Model | In fact we have a very similar question here on regression case. And we had a very good answer by @Matthew Drury
Gradient Boosting for Linear Regression - why does it not work?
Linear model (such as l | Boosting A Logistic Regression Model
In fact we have a very similar question here on regression case. And we had a very good answer by @Matthew Drury
Gradient Boosting for Linear Regression - why does it not work?
Linear model (such as logistic regression) is not good for boosting. The reason is if you add two linear m... | Boosting A Logistic Regression Model
In fact we have a very similar question here on regression case. And we had a very good answer by @Matthew Drury
Gradient Boosting for Linear Regression - why does it not work?
Linear model (such as l |
15,720 | Data augmentation on training set only? | In terms of the concept of augmentation, ie making the data set bigger for some reason, we'd tend to only augment the training set. We'd evaluate the result of different augmentation approaches on a validation set.
However, as @Łukasz Grad points out, we might need to perform a similar procedure to the test set as was ... | Data augmentation on training set only? | In terms of the concept of augmentation, ie making the data set bigger for some reason, we'd tend to only augment the training set. We'd evaluate the result of different augmentation approaches on a v | Data augmentation on training set only?
In terms of the concept of augmentation, ie making the data set bigger for some reason, we'd tend to only augment the training set. We'd evaluate the result of different augmentation approaches on a validation set.
However, as @Łukasz Grad points out, we might need to perform a s... | Data augmentation on training set only?
In terms of the concept of augmentation, ie making the data set bigger for some reason, we'd tend to only augment the training set. We'd evaluate the result of different augmentation approaches on a v |
15,721 | Data augmentation on training set only? | Typically, data augmentation for training convolutional neural networks is only done to the training set. I'm not sure what benefit augmenting the test data would achieve as the value of test data is primarily for model selection and evaluation and you're adding noise to your measurement of those quantities. | Data augmentation on training set only? | Typically, data augmentation for training convolutional neural networks is only done to the training set. I'm not sure what benefit augmenting the test data would achieve as the value of test data is | Data augmentation on training set only?
Typically, data augmentation for training convolutional neural networks is only done to the training set. I'm not sure what benefit augmenting the test data would achieve as the value of test data is primarily for model selection and evaluation and you're adding noise to your mea... | Data augmentation on training set only?
Typically, data augmentation for training convolutional neural networks is only done to the training set. I'm not sure what benefit augmenting the test data would achieve as the value of test data is |
15,722 | Data augmentation on training set only? | Complementing the answers, let my add my 2 cents regarding test-time data augmentation.
Data augmentation can be also performed during test-time with the goal of reducing variance. It can be performed by taking the average of the predictions of modified versions of the input image.
Dataset augmentation may be seen as ... | Data augmentation on training set only? | Complementing the answers, let my add my 2 cents regarding test-time data augmentation.
Data augmentation can be also performed during test-time with the goal of reducing variance. It can be performed | Data augmentation on training set only?
Complementing the answers, let my add my 2 cents regarding test-time data augmentation.
Data augmentation can be also performed during test-time with the goal of reducing variance. It can be performed by taking the average of the predictions of modified versions of the input imag... | Data augmentation on training set only?
Complementing the answers, let my add my 2 cents regarding test-time data augmentation.
Data augmentation can be also performed during test-time with the goal of reducing variance. It can be performed |
15,723 | What are real life examples of "non-parametric statistical models"? | As Johnnyboycurtis has answerd, non-parametric methods are those if it makes no assumption on the population distribution or sample size to generate a model.
A k-NN model is an example of a non-parametric model as it does not consider any assumptions to develop a model. A Naive Bayes or K-means is an example of parame... | What are real life examples of "non-parametric statistical models"? | As Johnnyboycurtis has answerd, non-parametric methods are those if it makes no assumption on the population distribution or sample size to generate a model.
A k-NN model is an example of a non-param | What are real life examples of "non-parametric statistical models"?
As Johnnyboycurtis has answerd, non-parametric methods are those if it makes no assumption on the population distribution or sample size to generate a model.
A k-NN model is an example of a non-parametric model as it does not consider any assumptions ... | What are real life examples of "non-parametric statistical models"?
As Johnnyboycurtis has answerd, non-parametric methods are those if it makes no assumption on the population distribution or sample size to generate a model.
A k-NN model is an example of a non-param |
15,724 | What are real life examples of "non-parametric statistical models"? | I'm currently taking a course on Machine learning, where we use the following definition of nonparametric models:
"Nonparametric models grow in complexity with the size of the data".
Parametric model
To see what it mean let's have a look at linear regression, a parametric model:
There we try to predict a function param... | What are real life examples of "non-parametric statistical models"? | I'm currently taking a course on Machine learning, where we use the following definition of nonparametric models:
"Nonparametric models grow in complexity with the size of the data".
Parametric model
| What are real life examples of "non-parametric statistical models"?
I'm currently taking a course on Machine learning, where we use the following definition of nonparametric models:
"Nonparametric models grow in complexity with the size of the data".
Parametric model
To see what it mean let's have a look at linear regr... | What are real life examples of "non-parametric statistical models"?
I'm currently taking a course on Machine learning, where we use the following definition of nonparametric models:
"Nonparametric models grow in complexity with the size of the data".
Parametric model
|
15,725 | What are real life examples of "non-parametric statistical models"? | So, I think you're missing a few points. First, and most importantly,
A statistical method is called non-parametric if it makes no
assumption on the population distribution or sample size.
Here is a simple (applied) tutorial on some nonparmetric models: http://www.r-tutor.com/elementary-statistics/non-parametric-m... | What are real life examples of "non-parametric statistical models"? | So, I think you're missing a few points. First, and most importantly,
A statistical method is called non-parametric if it makes no
assumption on the population distribution or sample size.
Here i | What are real life examples of "non-parametric statistical models"?
So, I think you're missing a few points. First, and most importantly,
A statistical method is called non-parametric if it makes no
assumption on the population distribution or sample size.
Here is a simple (applied) tutorial on some nonparmetric m... | What are real life examples of "non-parametric statistical models"?
So, I think you're missing a few points. First, and most importantly,
A statistical method is called non-parametric if it makes no
assumption on the population distribution or sample size.
Here i |
15,726 | How do I detrend time series? | If the trend is deterministic (e.g. a linear trend) you could run a regression of the data on the deterministic trend (e.g. a constant plus time index) to estimate the trend and remove it from the data. If the trend is stochastic you should detrend the series by taking first differences on it.
The ADF test and the KPSS... | How do I detrend time series? | If the trend is deterministic (e.g. a linear trend) you could run a regression of the data on the deterministic trend (e.g. a constant plus time index) to estimate the trend and remove it from the dat | How do I detrend time series?
If the trend is deterministic (e.g. a linear trend) you could run a regression of the data on the deterministic trend (e.g. a constant plus time index) to estimate the trend and remove it from the data. If the trend is stochastic you should detrend the series by taking first differences on... | How do I detrend time series?
If the trend is deterministic (e.g. a linear trend) you could run a regression of the data on the deterministic trend (e.g. a constant plus time index) to estimate the trend and remove it from the dat |
15,727 | How do I detrend time series? | Perhaps there is more than one trend . Perhaps there is a level shift . Perhaps the error variance has changed over time In any case a simple de-trending might be inappropriate. Good exploratory analysis along the line of http://www.unc.edu/~jbhill/tsay.pdf should be used to discover the nature of the data/model. | How do I detrend time series? | Perhaps there is more than one trend . Perhaps there is a level shift . Perhaps the error variance has changed over time In any case a simple de-trending might be inappropriate. Good exploratory anal | How do I detrend time series?
Perhaps there is more than one trend . Perhaps there is a level shift . Perhaps the error variance has changed over time In any case a simple de-trending might be inappropriate. Good exploratory analysis along the line of http://www.unc.edu/~jbhill/tsay.pdf should be used to discover the ... | How do I detrend time series?
Perhaps there is more than one trend . Perhaps there is a level shift . Perhaps the error variance has changed over time In any case a simple de-trending might be inappropriate. Good exploratory anal |
15,728 | How do I detrend time series? | You have several ways of detrending a time-series with the aim of making it stationary:
The linear detrending is what you copied. It may not give you what you desire as you arbitrarily fix a deterministic linear trend.
The quadratic detrending is in some ways similar to the linear detrending, except that you add a "ti... | How do I detrend time series? | You have several ways of detrending a time-series with the aim of making it stationary:
The linear detrending is what you copied. It may not give you what you desire as you arbitrarily fix a determin | How do I detrend time series?
You have several ways of detrending a time-series with the aim of making it stationary:
The linear detrending is what you copied. It may not give you what you desire as you arbitrarily fix a deterministic linear trend.
The quadratic detrending is in some ways similar to the linear detrend... | How do I detrend time series?
You have several ways of detrending a time-series with the aim of making it stationary:
The linear detrending is what you copied. It may not give you what you desire as you arbitrarily fix a determin |
15,729 | How do I detrend time series? | I suggest to take a look at Singular Spectrum analysis. It is a nonparametric technique which can be very roughly seen as PCA for time series. One of useful properties is that it can effectively de-trend series. | How do I detrend time series? | I suggest to take a look at Singular Spectrum analysis. It is a nonparametric technique which can be very roughly seen as PCA for time series. One of useful properties is that it can effectively de-tr | How do I detrend time series?
I suggest to take a look at Singular Spectrum analysis. It is a nonparametric technique which can be very roughly seen as PCA for time series. One of useful properties is that it can effectively de-trend series. | How do I detrend time series?
I suggest to take a look at Singular Spectrum analysis. It is a nonparametric technique which can be very roughly seen as PCA for time series. One of useful properties is that it can effectively de-tr |
15,730 | How do I detrend time series? | You need to research this subject carefully and can start here.
http://www.stat.pitt.edu/stoffer/tsa3/
The key thing you are looking for is stationarity or non-stationarity because most statistical tests assume that data is distributed normally. There are different ways to transform data to make it stationary. Detrendi... | How do I detrend time series? | You need to research this subject carefully and can start here.
http://www.stat.pitt.edu/stoffer/tsa3/
The key thing you are looking for is stationarity or non-stationarity because most statistical te | How do I detrend time series?
You need to research this subject carefully and can start here.
http://www.stat.pitt.edu/stoffer/tsa3/
The key thing you are looking for is stationarity or non-stationarity because most statistical tests assume that data is distributed normally. There are different ways to transform data t... | How do I detrend time series?
You need to research this subject carefully and can start here.
http://www.stat.pitt.edu/stoffer/tsa3/
The key thing you are looking for is stationarity or non-stationarity because most statistical te |
15,731 | Sum of Bernoulli variables with different success probabilities [duplicate] | Yes, in fact, the distribution is known as the Poisson binomial distribution, which is a generalization of the binomial distribution. The distribution's mean and variance are intuitive and are given by
$$
\begin{align}
E\left[\sum_i x_i\right] &= \sum_i E[x_i] = \sum_i p_i\\
V\left[\sum_i x_i\right] &= \sum_i V[x_i] = ... | Sum of Bernoulli variables with different success probabilities [duplicate] | Yes, in fact, the distribution is known as the Poisson binomial distribution, which is a generalization of the binomial distribution. The distribution's mean and variance are intuitive and are given b | Sum of Bernoulli variables with different success probabilities [duplicate]
Yes, in fact, the distribution is known as the Poisson binomial distribution, which is a generalization of the binomial distribution. The distribution's mean and variance are intuitive and are given by
$$
\begin{align}
E\left[\sum_i x_i\right] ... | Sum of Bernoulli variables with different success probabilities [duplicate]
Yes, in fact, the distribution is known as the Poisson binomial distribution, which is a generalization of the binomial distribution. The distribution's mean and variance are intuitive and are given b |
15,732 | Sum of Bernoulli variables with different success probabilities [duplicate] | I'm not aware of a closed formula to exist. If n becomes relevant you can apply Central Theorem Limit so approximating the sum distribution with a normal distribution having mean the sum of p_i and variance the sum of p_i * ( 1 - p_i). | Sum of Bernoulli variables with different success probabilities [duplicate] | I'm not aware of a closed formula to exist. If n becomes relevant you can apply Central Theorem Limit so approximating the sum distribution with a normal distribution having mean the sum of p_i and va | Sum of Bernoulli variables with different success probabilities [duplicate]
I'm not aware of a closed formula to exist. If n becomes relevant you can apply Central Theorem Limit so approximating the sum distribution with a normal distribution having mean the sum of p_i and variance the sum of p_i * ( 1 - p_i). | Sum of Bernoulli variables with different success probabilities [duplicate]
I'm not aware of a closed formula to exist. If n becomes relevant you can apply Central Theorem Limit so approximating the sum distribution with a normal distribution having mean the sum of p_i and va |
15,733 | How to estimate Poisson process using R? (Or: how to use NHPoisson package?) | Fitting a stationary Poisson process
First of all it is important to realize, what sort of input data NHPoisson needs.
Foremost, NHPoisson needs a list of indices of event moments. If we record time interval and number of events in the time interval, than I we must translate it into a single column of dates, possibly ... | How to estimate Poisson process using R? (Or: how to use NHPoisson package?) | Fitting a stationary Poisson process
First of all it is important to realize, what sort of input data NHPoisson needs.
Foremost, NHPoisson needs a list of indices of event moments. If we record time | How to estimate Poisson process using R? (Or: how to use NHPoisson package?)
Fitting a stationary Poisson process
First of all it is important to realize, what sort of input data NHPoisson needs.
Foremost, NHPoisson needs a list of indices of event moments. If we record time interval and number of events in the time i... | How to estimate Poisson process using R? (Or: how to use NHPoisson package?)
Fitting a stationary Poisson process
First of all it is important to realize, what sort of input data NHPoisson needs.
Foremost, NHPoisson needs a list of indices of event moments. If we record time |
15,734 | Does it make sense for a partial correlation to be larger than a zero-order correlation? | Looking at the wikipedia page we have the partial correlation between $X$ and $Y$ given $Z$ is given by:
$$\rho_{XY|Z}=\frac{\rho_{XY}-\rho_{XZ}\rho_{YZ}}{\sqrt{1-\rho_{XZ}^{2}}\sqrt{1-\rho_{YZ}^{2}}}>\rho_{XY}$$
So we simply require
$$\rho_{XY}>\frac{\rho_{XZ}\rho_{YZ}}{1-\sqrt{1-\rho_{XZ}^{2}}\sqrt{1-\rho_{YZ}^{2}}}$... | Does it make sense for a partial correlation to be larger than a zero-order correlation? | Looking at the wikipedia page we have the partial correlation between $X$ and $Y$ given $Z$ is given by:
$$\rho_{XY|Z}=\frac{\rho_{XY}-\rho_{XZ}\rho_{YZ}}{\sqrt{1-\rho_{XZ}^{2}}\sqrt{1-\rho_{YZ}^{2}}} | Does it make sense for a partial correlation to be larger than a zero-order correlation?
Looking at the wikipedia page we have the partial correlation between $X$ and $Y$ given $Z$ is given by:
$$\rho_{XY|Z}=\frac{\rho_{XY}-\rho_{XZ}\rho_{YZ}}{\sqrt{1-\rho_{XZ}^{2}}\sqrt{1-\rho_{YZ}^{2}}}>\rho_{XY}$$
So we simply requi... | Does it make sense for a partial correlation to be larger than a zero-order correlation?
Looking at the wikipedia page we have the partial correlation between $X$ and $Y$ given $Z$ is given by:
$$\rho_{XY|Z}=\frac{\rho_{XY}-\rho_{XZ}\rho_{YZ}}{\sqrt{1-\rho_{XZ}^{2}}\sqrt{1-\rho_{YZ}^{2}}} |
15,735 | Does it make sense for a partial correlation to be larger than a zero-order correlation? | People are more prone to think of confounders and mediators when it comes to these situations, in the sense that by adjusting for them you're blocking paths, removing indirect associations and therefore getting the direct correlation between the two variables of interest. The issue is that sometimes the variable we are... | Does it make sense for a partial correlation to be larger than a zero-order correlation? | People are more prone to think of confounders and mediators when it comes to these situations, in the sense that by adjusting for them you're blocking paths, removing indirect associations and therefo | Does it make sense for a partial correlation to be larger than a zero-order correlation?
People are more prone to think of confounders and mediators when it comes to these situations, in the sense that by adjusting for them you're blocking paths, removing indirect associations and therefore getting the direct correlati... | Does it make sense for a partial correlation to be larger than a zero-order correlation?
People are more prone to think of confounders and mediators when it comes to these situations, in the sense that by adjusting for them you're blocking paths, removing indirect associations and therefo |
15,736 | Does it make sense for a partial correlation to be larger than a zero-order correlation? | I think that variable z in the question is a suppresor variable.
I suggest having a look at:
Tzelgov, J., & Henik, A. (1991).Suppression situations in psychological research: Definitions, implications, and applications, Psychological Bulletin, 109 (3), 524-536. http://doi.apa.org/psycinfo/1991-20289-001
See also: http... | Does it make sense for a partial correlation to be larger than a zero-order correlation? | I think that variable z in the question is a suppresor variable.
I suggest having a look at:
Tzelgov, J., & Henik, A. (1991).Suppression situations in psychological research: Definitions, implication | Does it make sense for a partial correlation to be larger than a zero-order correlation?
I think that variable z in the question is a suppresor variable.
I suggest having a look at:
Tzelgov, J., & Henik, A. (1991).Suppression situations in psychological research: Definitions, implications, and applications, Psychologi... | Does it make sense for a partial correlation to be larger than a zero-order correlation?
I think that variable z in the question is a suppresor variable.
I suggest having a look at:
Tzelgov, J., & Henik, A. (1991).Suppression situations in psychological research: Definitions, implication |
15,737 | Does it make sense for a partial correlation to be larger than a zero-order correlation? | I think you need to know about moderator and mediator variables. The classic paper is Baron and Kenny [cited 21,659 times]
A moderator variable
"In general terms, a moderator is a
qualitative (e.g., sex, race, class)
or quantitative (e.g., level of
reward) variable that affects the
direction and/or strength o... | Does it make sense for a partial correlation to be larger than a zero-order correlation? | I think you need to know about moderator and mediator variables. The classic paper is Baron and Kenny [cited 21,659 times]
A moderator variable
"In general terms, a moderator is a
qualitative (e.g | Does it make sense for a partial correlation to be larger than a zero-order correlation?
I think you need to know about moderator and mediator variables. The classic paper is Baron and Kenny [cited 21,659 times]
A moderator variable
"In general terms, a moderator is a
qualitative (e.g., sex, race, class)
or quant... | Does it make sense for a partial correlation to be larger than a zero-order correlation?
I think you need to know about moderator and mediator variables. The classic paper is Baron and Kenny [cited 21,659 times]
A moderator variable
"In general terms, a moderator is a
qualitative (e.g |
15,738 | Fitting models in R where coefficients are subject to linear restriction(s)? | Suppose your model is
$ Y(t) = \beta_0 + \beta_1 \cdot X_1(t) + \beta_2 \cdot X_2(t) + \varepsilon(t)$
and you are planning to restrict the coefficients, for instance like:
$ \beta_1 = 2 \beta_2$
inserting the restriction, rewriting the original regression model you will get
$ Y(t) = \beta_0 + 2 \beta_2 \cdot X_1(t) + ... | Fitting models in R where coefficients are subject to linear restriction(s)? | Suppose your model is
$ Y(t) = \beta_0 + \beta_1 \cdot X_1(t) + \beta_2 \cdot X_2(t) + \varepsilon(t)$
and you are planning to restrict the coefficients, for instance like:
$ \beta_1 = 2 \beta_2$
inse | Fitting models in R where coefficients are subject to linear restriction(s)?
Suppose your model is
$ Y(t) = \beta_0 + \beta_1 \cdot X_1(t) + \beta_2 \cdot X_2(t) + \varepsilon(t)$
and you are planning to restrict the coefficients, for instance like:
$ \beta_1 = 2 \beta_2$
inserting the restriction, rewriting the origin... | Fitting models in R where coefficients are subject to linear restriction(s)?
Suppose your model is
$ Y(t) = \beta_0 + \beta_1 \cdot X_1(t) + \beta_2 \cdot X_2(t) + \varepsilon(t)$
and you are planning to restrict the coefficients, for instance like:
$ \beta_1 = 2 \beta_2$
inse |
15,739 | Why binary crossentropy can be used as the loss function in autoencoders? [duplicate] | I thought a regression loss function such as mean squared error or
mean absolute error must be used instead, which have a value of zero
when labels and predictions are the same.
That's exactly the misconception you have. You think that in order for a loss function to be used in a model like an autoencoder, it must... | Why binary crossentropy can be used as the loss function in autoencoders? [duplicate] | I thought a regression loss function such as mean squared error or
mean absolute error must be used instead, which have a value of zero
when labels and predictions are the same.
That's exactly th | Why binary crossentropy can be used as the loss function in autoencoders? [duplicate]
I thought a regression loss function such as mean squared error or
mean absolute error must be used instead, which have a value of zero
when labels and predictions are the same.
That's exactly the misconception you have. You thin... | Why binary crossentropy can be used as the loss function in autoencoders? [duplicate]
I thought a regression loss function such as mean squared error or
mean absolute error must be used instead, which have a value of zero
when labels and predictions are the same.
That's exactly th |
15,740 | Why binary crossentropy can be used as the loss function in autoencoders? [duplicate] | As @today pointed out, loss value doesn't have to be 0 when the solution is optimal, it is enough that it is minimal.
One thing I would like to add is why one would prefer binary crossentropy over MSE. Normally, the activation function of the last layer is sigmoid, which can lead to loss saturation ("plateau"). This sa... | Why binary crossentropy can be used as the loss function in autoencoders? [duplicate] | As @today pointed out, loss value doesn't have to be 0 when the solution is optimal, it is enough that it is minimal.
One thing I would like to add is why one would prefer binary crossentropy over MSE | Why binary crossentropy can be used as the loss function in autoencoders? [duplicate]
As @today pointed out, loss value doesn't have to be 0 when the solution is optimal, it is enough that it is minimal.
One thing I would like to add is why one would prefer binary crossentropy over MSE. Normally, the activation functio... | Why binary crossentropy can be used as the loss function in autoencoders? [duplicate]
As @today pointed out, loss value doesn't have to be 0 when the solution is optimal, it is enough that it is minimal.
One thing I would like to add is why one would prefer binary crossentropy over MSE |
15,741 | What does a 'tractable' distribution mean? | To my best memory, I've never come across a formal definition for this in a statistical text, but I think you can stitch one together from a few contextual readings. Start with Bayesian Data Analysis, p. 261:
Bayesian computation revolves around two steps: computation of the posterior distribution, $p(\theta|y)$, and ... | What does a 'tractable' distribution mean? | To my best memory, I've never come across a formal definition for this in a statistical text, but I think you can stitch one together from a few contextual readings. Start with Bayesian Data Analysis, | What does a 'tractable' distribution mean?
To my best memory, I've never come across a formal definition for this in a statistical text, but I think you can stitch one together from a few contextual readings. Start with Bayesian Data Analysis, p. 261:
Bayesian computation revolves around two steps: computation of the ... | What does a 'tractable' distribution mean?
To my best memory, I've never come across a formal definition for this in a statistical text, but I think you can stitch one together from a few contextual readings. Start with Bayesian Data Analysis, |
15,742 | What does a 'tractable' distribution mean? | In addition to Sean Easter's answer, I will try to shed some light from the perspective of computational cost.
First of all, let's define what tractable and intractable problems are (Reference: http://www.cs.ucc.ie/~dgb/courses/toc/handout29.pdf).
Tractable Problem: a problem that is solvable by a polynomial-time algo... | What does a 'tractable' distribution mean? | In addition to Sean Easter's answer, I will try to shed some light from the perspective of computational cost.
First of all, let's define what tractable and intractable problems are (Reference: http:/ | What does a 'tractable' distribution mean?
In addition to Sean Easter's answer, I will try to shed some light from the perspective of computational cost.
First of all, let's define what tractable and intractable problems are (Reference: http://www.cs.ucc.ie/~dgb/courses/toc/handout29.pdf).
Tractable Problem: a problem... | What does a 'tractable' distribution mean?
In addition to Sean Easter's answer, I will try to shed some light from the perspective of computational cost.
First of all, let's define what tractable and intractable problems are (Reference: http:/ |
15,743 | What does a 'tractable' distribution mean? | A distribution is called tractable if any marginal probability induced by it can be computed in linear time | What does a 'tractable' distribution mean? | A distribution is called tractable if any marginal probability induced by it can be computed in linear time | What does a 'tractable' distribution mean?
A distribution is called tractable if any marginal probability induced by it can be computed in linear time | What does a 'tractable' distribution mean?
A distribution is called tractable if any marginal probability induced by it can be computed in linear time |
15,744 | Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and how this makes linear separation possible | Let $h(x)$ be the projection to high dimension space $\mathcal{F}$. Basically the kernel function $K(x_1,x_2)=\langle h(x_1),h(x_2)\rangle$, which is the inner-product. So it's not used to project data points, but rather an outcome of the projection. It can be considered a measure of similarity, but in an SVM, it's mo... | Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and | Let $h(x)$ be the projection to high dimension space $\mathcal{F}$. Basically the kernel function $K(x_1,x_2)=\langle h(x_1),h(x_2)\rangle$, which is the inner-product. So it's not used to project da | Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and how this makes linear separation possible
Let $h(x)$ be the projection to high dimension space $\mathcal{F}$. Basically the kernel function $K(x_1,x_2)=\langle h(x_1),h(x_2)\rangle$, which is the inner-product. So it's ... | Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and
Let $h(x)$ be the projection to high dimension space $\mathcal{F}$. Basically the kernel function $K(x_1,x_2)=\langle h(x_1),h(x_2)\rangle$, which is the inner-product. So it's not used to project da |
15,745 | Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and how this makes linear separation possible | The useful properties of kernel SVM are not universal - they depend on the choice of kernel. To get intuition it's helpful to look at one of the most commonly used kernels, the Gaussian kernel. Remarkably, this kernel turns SVM into something very much like a k-nearest neighbor classifier.
This answer explains the fol... | Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and | The useful properties of kernel SVM are not universal - they depend on the choice of kernel. To get intuition it's helpful to look at one of the most commonly used kernels, the Gaussian kernel. Remark | Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and how this makes linear separation possible
The useful properties of kernel SVM are not universal - they depend on the choice of kernel. To get intuition it's helpful to look at one of the most commonly used kernels, the G... | Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and
The useful properties of kernel SVM are not universal - they depend on the choice of kernel. To get intuition it's helpful to look at one of the most commonly used kernels, the Gaussian kernel. Remark |
15,746 | Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and how this makes linear separation possible | For the background and the notations I refer to How to calculate decision boundary from support vectors?.
So the features in the 'original' space are the vectors $x_i$, the binary outcome $y_i \in \{-1, +1\}$ and the Lagrange multipliers are $\alpha_i$.
As said by @Lii (+1) the Kernel can be written as $K(x,y)=h(x) \... | Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and | For the background and the notations I refer to How to calculate decision boundary from support vectors?.
So the features in the 'original' space are the vectors $x_i$, the binary outcome $y_i \in \{ | Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and how this makes linear separation possible
For the background and the notations I refer to How to calculate decision boundary from support vectors?.
So the features in the 'original' space are the vectors $x_i$, the bina... | Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and
For the background and the notations I refer to How to calculate decision boundary from support vectors?.
So the features in the 'original' space are the vectors $x_i$, the binary outcome $y_i \in \{ |
15,747 | Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and how this makes linear separation possible | Transform predictors (input data) to a high-dimensional feature space. It is sufficient to just specify the kernel for this step and the data is never explicitly transformed to the feature space. This process is commonly known as the kernel trick.
Let me explain it. The kernel trick is the key here. Consider the case ... | Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and | Transform predictors (input data) to a high-dimensional feature space. It is sufficient to just specify the kernel for this step and the data is never explicitly transformed to the feature space. This | Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and how this makes linear separation possible
Transform predictors (input data) to a high-dimensional feature space. It is sufficient to just specify the kernel for this step and the data is never explicitly transformed to t... | Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and
Transform predictors (input data) to a high-dimensional feature space. It is sufficient to just specify the kernel for this step and the data is never explicitly transformed to the feature space. This |
15,748 | Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and how this makes linear separation possible | Mapping to a higher dimension is merely a trick to solve a problem that is defined in the original dimension; so concerns such as overfitting your data by going into a dimension with too many degrees of freedom are not a byproduct of the mapping process, but are inherent in your problem definition.
Basically, all that ... | Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and | Mapping to a higher dimension is merely a trick to solve a problem that is defined in the original dimension; so concerns such as overfitting your data by going into a dimension with too many degrees | Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and how this makes linear separation possible
Mapping to a higher dimension is merely a trick to solve a problem that is defined in the original dimension; so concerns such as overfitting your data by going into a dimension ... | Kernel SVM: I want an intuitive understanding of mapping to a higher-dimensional feature space, and
Mapping to a higher dimension is merely a trick to solve a problem that is defined in the original dimension; so concerns such as overfitting your data by going into a dimension with too many degrees |
15,749 | How does regularization reduce overfitting? [duplicate] | This is related to the Bias-Variance tradeoff. The expected error can be decomposed as
$$
\mathrm{E}[(y - f(x))^2] = \mathrm{Bias}(f(x))^2 + \mathrm{Var}(f(x)) + \sigma^2,
$$
where the bias is the systematic deviation of our estimator, $f$, from the true value, i.e. $E[f^* - f]$, where $f^*$ is the true estimator, ... | How does regularization reduce overfitting? [duplicate] | This is related to the Bias-Variance tradeoff. The expected error can be decomposed as
$$
\mathrm{E}[(y - f(x))^2] = \mathrm{Bias}(f(x))^2 + \mathrm{Var}(f(x)) + \sigma^2,
$$
where the bias is the | How does regularization reduce overfitting? [duplicate]
This is related to the Bias-Variance tradeoff. The expected error can be decomposed as
$$
\mathrm{E}[(y - f(x))^2] = \mathrm{Bias}(f(x))^2 + \mathrm{Var}(f(x)) + \sigma^2,
$$
where the bias is the systematic deviation of our estimator, $f$, from the true value... | How does regularization reduce overfitting? [duplicate]
This is related to the Bias-Variance tradeoff. The expected error can be decomposed as
$$
\mathrm{E}[(y - f(x))^2] = \mathrm{Bias}(f(x))^2 + \mathrm{Var}(f(x)) + \sigma^2,
$$
where the bias is the |
15,750 | From exp (coefficients) to Odds Ratio and their interpretation in Logistic Regression with factors | I've been working on answering my question by calculating manually the odds and odds ratios:
Acceptance blue red Grand Total
0 158 102 260
1 112 177 289
Total 270 279 549
So the Odds Ra... | From exp (coefficients) to Odds Ratio and their interpretation in Logistic Regression with factors | I've been working on answering my question by calculating manually the odds and odds ratios:
Acceptance blue red Grand Total
0 158 102 260 | From exp (coefficients) to Odds Ratio and their interpretation in Logistic Regression with factors
I've been working on answering my question by calculating manually the odds and odds ratios:
Acceptance blue red Grand Total
0 158 102 260
1 112 ... | From exp (coefficients) to Odds Ratio and their interpretation in Logistic Regression with factors
I've been working on answering my question by calculating manually the odds and odds ratios:
Acceptance blue red Grand Total
0 158 102 260 |
15,751 | Choosing optimal K for KNN | If you carry on going, you will eventually end up with the CV error beginning to go up again. This is because the larger you make $k$, the more smoothing takes place, and eventually you will smooth so much that you will get a model that under-fits the data rather than over-fitting it (make $k$ big enough and the outpu... | Choosing optimal K for KNN | If you carry on going, you will eventually end up with the CV error beginning to go up again. This is because the larger you make $k$, the more smoothing takes place, and eventually you will smooth s | Choosing optimal K for KNN
If you carry on going, you will eventually end up with the CV error beginning to go up again. This is because the larger you make $k$, the more smoothing takes place, and eventually you will smooth so much that you will get a model that under-fits the data rather than over-fitting it (make $... | Choosing optimal K for KNN
If you carry on going, you will eventually end up with the CV error beginning to go up again. This is because the larger you make $k$, the more smoothing takes place, and eventually you will smooth s |
15,752 | Choosing optimal K for KNN | Why not choose $K=17$? It looks like the CV error goes down until then and flattens out afterwards. If all you care about is predictive accuracy then I would not choose $K=3$ because it looks pretty clear that you can do better. | Choosing optimal K for KNN | Why not choose $K=17$? It looks like the CV error goes down until then and flattens out afterwards. If all you care about is predictive accuracy then I would not choose $K=3$ because it looks pretty c | Choosing optimal K for KNN
Why not choose $K=17$? It looks like the CV error goes down until then and flattens out afterwards. If all you care about is predictive accuracy then I would not choose $K=3$ because it looks pretty clear that you can do better. | Choosing optimal K for KNN
Why not choose $K=17$? It looks like the CV error goes down until then and flattens out afterwards. If all you care about is predictive accuracy then I would not choose $K=3$ because it looks pretty c |
15,753 | Choosing optimal K for KNN | Is there any physical or natural meaning behind the number of clusters? If I am not wrong, it is only natural that as K increases, error decreases - kind of like overfitting. Rather than fishing for the optimal K, its probably better to pick K based on domain knowledge or some intuition? | Choosing optimal K for KNN | Is there any physical or natural meaning behind the number of clusters? If I am not wrong, it is only natural that as K increases, error decreases - kind of like overfitting. Rather than fishing for t | Choosing optimal K for KNN
Is there any physical or natural meaning behind the number of clusters? If I am not wrong, it is only natural that as K increases, error decreases - kind of like overfitting. Rather than fishing for the optimal K, its probably better to pick K based on domain knowledge or some intuition? | Choosing optimal K for KNN
Is there any physical or natural meaning behind the number of clusters? If I am not wrong, it is only natural that as K increases, error decreases - kind of like overfitting. Rather than fishing for t |
15,754 | When is Fisher's z-transform appropriate? | For questions like these I would just run a simulation and see if the $p$-values behave as I expect them to. The $p$-value is the probability of randomly drawing a sample that deviates at least as much from the null-hypothesis as the data you observed if the null-hypothesis is true. So if we had many such samples, and ... | When is Fisher's z-transform appropriate? | For questions like these I would just run a simulation and see if the $p$-values behave as I expect them to. The $p$-value is the probability of randomly drawing a sample that deviates at least as muc | When is Fisher's z-transform appropriate?
For questions like these I would just run a simulation and see if the $p$-values behave as I expect them to. The $p$-value is the probability of randomly drawing a sample that deviates at least as much from the null-hypothesis as the data you observed if the null-hypothesis is ... | When is Fisher's z-transform appropriate?
For questions like these I would just run a simulation and see if the $p$-values behave as I expect them to. The $p$-value is the probability of randomly drawing a sample that deviates at least as muc |
15,755 | When is Fisher's z-transform appropriate? | FWIW I see the recommendation $N\ge 10$ in Myers & Well (research design and statistical analyses, second edition, 2003, p. 492). The footnote states:
Strictly speaking, the $Z$ transformation is biased by an amount $r/(2(N-1))$: see Pearson and Hartley (1954, p. 29). This bias will generally be negligible unless $N$... | When is Fisher's z-transform appropriate? | FWIW I see the recommendation $N\ge 10$ in Myers & Well (research design and statistical analyses, second edition, 2003, p. 492). The footnote states:
Strictly speaking, the $Z$ transformation is bi | When is Fisher's z-transform appropriate?
FWIW I see the recommendation $N\ge 10$ in Myers & Well (research design and statistical analyses, second edition, 2003, p. 492). The footnote states:
Strictly speaking, the $Z$ transformation is biased by an amount $r/(2(N-1))$: see Pearson and Hartley (1954, p. 29). This bi... | When is Fisher's z-transform appropriate?
FWIW I see the recommendation $N\ge 10$ in Myers & Well (research design and statistical analyses, second edition, 2003, p. 492). The footnote states:
Strictly speaking, the $Z$ transformation is bi |
15,756 | When is Fisher's z-transform appropriate? | Not sure whether a Fisher's $z$ transform is appropriate here. For $H_0: \rho=0$ (NB: null hypothesis is for population $\rho$, not sample $r$), the sampling distribution of the correlation coefficient is already symmetric, so no need to reduce skewness, which is what Fisher's $z$ aims to do, and you can use Student's ... | When is Fisher's z-transform appropriate? | Not sure whether a Fisher's $z$ transform is appropriate here. For $H_0: \rho=0$ (NB: null hypothesis is for population $\rho$, not sample $r$), the sampling distribution of the correlation coefficien | When is Fisher's z-transform appropriate?
Not sure whether a Fisher's $z$ transform is appropriate here. For $H_0: \rho=0$ (NB: null hypothesis is for population $\rho$, not sample $r$), the sampling distribution of the correlation coefficient is already symmetric, so no need to reduce skewness, which is what Fisher's ... | When is Fisher's z-transform appropriate?
Not sure whether a Fisher's $z$ transform is appropriate here. For $H_0: \rho=0$ (NB: null hypothesis is for population $\rho$, not sample $r$), the sampling distribution of the correlation coefficien |
15,757 | Are robust methods really any better? | In short, and from your description, you are comparing apple to oranges....in
two ways.
Let me address the first comparability issue briefly. The log transform does not address the outlier problem. However, it can help making heavily skewed data more symmetric, potentially improving the fit of any PCA method. In shor... | Are robust methods really any better? | In short, and from your description, you are comparing apple to oranges....in
two ways.
Let me address the first comparability issue briefly. The log transform does not address the outlier problem. | Are robust methods really any better?
In short, and from your description, you are comparing apple to oranges....in
two ways.
Let me address the first comparability issue briefly. The log transform does not address the outlier problem. However, it can help making heavily skewed data more symmetric, potentially improv... | Are robust methods really any better?
In short, and from your description, you are comparing apple to oranges....in
two ways.
Let me address the first comparability issue briefly. The log transform does not address the outlier problem. |
15,758 | Contingency tables: what tests to do and when? | This is a good question, but a big one. I don't think I can provide a complete answer, but I will throw out some food for thought.
First, under your top bullet point, the correction you are referring to is known as Yates' correction for continuity. The problem is that we calculate a discrete inferential statistic:
$... | Contingency tables: what tests to do and when? | This is a good question, but a big one. I don't think I can provide a complete answer, but I will throw out some food for thought.
First, under your top bullet point, the correction you are referri | Contingency tables: what tests to do and when?
This is a good question, but a big one. I don't think I can provide a complete answer, but I will throw out some food for thought.
First, under your top bullet point, the correction you are referring to is known as Yates' correction for continuity. The problem is that w... | Contingency tables: what tests to do and when?
This is a good question, but a big one. I don't think I can provide a complete answer, but I will throw out some food for thought.
First, under your top bullet point, the correction you are referri |
15,759 | Contingency tables: what tests to do and when? | I will try to address some of your questions as best as I can from my perspective. First the Fisher-Irwin Test is just another name for Fisher's exact test. Except for the fact that it is sometimes computationally intense I generally prefer to use the Fisher test. If there is any issue with this test it is conditioning... | Contingency tables: what tests to do and when? | I will try to address some of your questions as best as I can from my perspective. First the Fisher-Irwin Test is just another name for Fisher's exact test. Except for the fact that it is sometimes co | Contingency tables: what tests to do and when?
I will try to address some of your questions as best as I can from my perspective. First the Fisher-Irwin Test is just another name for Fisher's exact test. Except for the fact that it is sometimes computationally intense I generally prefer to use the Fisher test. If there... | Contingency tables: what tests to do and when?
I will try to address some of your questions as best as I can from my perspective. First the Fisher-Irwin Test is just another name for Fisher's exact test. Except for the fact that it is sometimes co |
15,760 | How is an ANOVA calculated for a repeated measures design: aov() vs lm() in R | One way to think about it is to treat the situation as a 3-factorial between subjects ANOVA with IVs participant, factor1, factor2, and a cell size of 1. anova(lm(value ~ factor1*factor2*participant, DFlong)) calculates all the SS for all effects in this 3-way ANOVA (3 main effects, 3 first-order interactions, 1 second... | How is an ANOVA calculated for a repeated measures design: aov() vs lm() in R | One way to think about it is to treat the situation as a 3-factorial between subjects ANOVA with IVs participant, factor1, factor2, and a cell size of 1. anova(lm(value ~ factor1*factor2*participant, | How is an ANOVA calculated for a repeated measures design: aov() vs lm() in R
One way to think about it is to treat the situation as a 3-factorial between subjects ANOVA with IVs participant, factor1, factor2, and a cell size of 1. anova(lm(value ~ factor1*factor2*participant, DFlong)) calculates all the SS for all eff... | How is an ANOVA calculated for a repeated measures design: aov() vs lm() in R
One way to think about it is to treat the situation as a 3-factorial between subjects ANOVA with IVs participant, factor1, factor2, and a cell size of 1. anova(lm(value ~ factor1*factor2*participant, |
15,761 | Estimating population size from the frequency of sampled duplicates and uniques | This is essentially a variant of the coupon collector's problem.
If there are $n$ items in total and you have taken a sample size $s$ with replacement then the probability of having identified $u$ unique items is
$$ Pr(U=u|n,s) = \frac{S_2(s,u) n! }{ (n-u)! n^s }$$
where $ S_2(s,u)$ gives Stirling numbers of the ... | Estimating population size from the frequency of sampled duplicates and uniques | This is essentially a variant of the coupon collector's problem.
If there are $n$ items in total and you have taken a sample size $s$ with replacement then the probability of having identified $u$ uni | Estimating population size from the frequency of sampled duplicates and uniques
This is essentially a variant of the coupon collector's problem.
If there are $n$ items in total and you have taken a sample size $s$ with replacement then the probability of having identified $u$ unique items is
$$ Pr(U=u|n,s) = \frac{... | Estimating population size from the frequency of sampled duplicates and uniques
This is essentially a variant of the coupon collector's problem.
If there are $n$ items in total and you have taken a sample size $s$ with replacement then the probability of having identified $u$ uni |
15,762 | Estimating population size from the frequency of sampled duplicates and uniques | I have already give a suggestion based on Stirling numbers of the second kind and Bayesian methods.
For those who find Stirling numbers too large or Bayesian methods too difficult, a rougher method might be to use
$$E[U|n,s] = n\left( 1- \left(1-\frac{1}{n}\right)^s\right)$$
$$var[U|n,s] = n\left(1-\frac{1}{n}\right... | Estimating population size from the frequency of sampled duplicates and uniques | I have already give a suggestion based on Stirling numbers of the second kind and Bayesian methods.
For those who find Stirling numbers too large or Bayesian methods too difficult, a rougher method | Estimating population size from the frequency of sampled duplicates and uniques
I have already give a suggestion based on Stirling numbers of the second kind and Bayesian methods.
For those who find Stirling numbers too large or Bayesian methods too difficult, a rougher method might be to use
$$E[U|n,s] = n\left( 1-... | Estimating population size from the frequency of sampled duplicates and uniques
I have already give a suggestion based on Stirling numbers of the second kind and Bayesian methods.
For those who find Stirling numbers too large or Bayesian methods too difficult, a rougher method |
15,763 | Estimating population size from the frequency of sampled duplicates and uniques | You can use the capture-recapture method, also implemented as the Rcapture R package.
Here is an example, coded in R. Let's assume that the web service has N=1000 items. We will make n=300 requests. Generate a random sample where, numbering the elements from 1 to k, where k is how many different items we saw.
N = 1000;... | Estimating population size from the frequency of sampled duplicates and uniques | You can use the capture-recapture method, also implemented as the Rcapture R package.
Here is an example, coded in R. Let's assume that the web service has N=1000 items. We will make n=300 requests. G | Estimating population size from the frequency of sampled duplicates and uniques
You can use the capture-recapture method, also implemented as the Rcapture R package.
Here is an example, coded in R. Let's assume that the web service has N=1000 items. We will make n=300 requests. Generate a random sample where, numbering... | Estimating population size from the frequency of sampled duplicates and uniques
You can use the capture-recapture method, also implemented as the Rcapture R package.
Here is an example, coded in R. Let's assume that the web service has N=1000 items. We will make n=300 requests. G |
15,764 | Intuitive explanation of contribution to sum of two normally distributed random variables | The question readily reduces to the case $\mu_X = \mu_Y = 0$ by looking at $X-\mu_X$ and $Y-\mu_Y$.
Clearly the conditional distributions are Normal. Thus, the mean, median, and mode of each are coincident. The modes will occur at the coordinates of a local maximum of the bivariate PDF of $X$ and $Y$ constrained to t... | Intuitive explanation of contribution to sum of two normally distributed random variables | The question readily reduces to the case $\mu_X = \mu_Y = 0$ by looking at $X-\mu_X$ and $Y-\mu_Y$.
Clearly the conditional distributions are Normal. Thus, the mean, median, and mode of each are coin | Intuitive explanation of contribution to sum of two normally distributed random variables
The question readily reduces to the case $\mu_X = \mu_Y = 0$ by looking at $X-\mu_X$ and $Y-\mu_Y$.
Clearly the conditional distributions are Normal. Thus, the mean, median, and mode of each are coincident. The modes will occur ... | Intuitive explanation of contribution to sum of two normally distributed random variables
The question readily reduces to the case $\mu_X = \mu_Y = 0$ by looking at $X-\mu_X$ and $Y-\mu_Y$.
Clearly the conditional distributions are Normal. Thus, the mean, median, and mode of each are coin |
15,765 | Assumptions of cluster analysis | Well, clustering techniques are not limited to distance-based methods where we seek groups of statistical units that are unusually close to each other, in a geometrical sense. There're also a range of techniques relying on density (clusters are seen as "regions" in the feature space) or probability distribution.
The l... | Assumptions of cluster analysis | Well, clustering techniques are not limited to distance-based methods where we seek groups of statistical units that are unusually close to each other, in a geometrical sense. There're also a range of | Assumptions of cluster analysis
Well, clustering techniques are not limited to distance-based methods where we seek groups of statistical units that are unusually close to each other, in a geometrical sense. There're also a range of techniques relying on density (clusters are seen as "regions" in the feature space) or ... | Assumptions of cluster analysis
Well, clustering techniques are not limited to distance-based methods where we seek groups of statistical units that are unusually close to each other, in a geometrical sense. There're also a range of |
15,766 | Assumptions of cluster analysis | There is a very wide variety of clustering methods, which are exploratory by nature, and I do not think that any of them, whether hierarchical or partition-based, relies on the kind of assumptions that one has to meet for analysing variance.
Having a look at the [MV] documentation in Stata to answer your question, I fo... | Assumptions of cluster analysis | There is a very wide variety of clustering methods, which are exploratory by nature, and I do not think that any of them, whether hierarchical or partition-based, relies on the kind of assumptions tha | Assumptions of cluster analysis
There is a very wide variety of clustering methods, which are exploratory by nature, and I do not think that any of them, whether hierarchical or partition-based, relies on the kind of assumptions that one has to meet for analysing variance.
Having a look at the [MV] documentation in Sta... | Assumptions of cluster analysis
There is a very wide variety of clustering methods, which are exploratory by nature, and I do not think that any of them, whether hierarchical or partition-based, relies on the kind of assumptions tha |
15,767 | Assumptions of cluster analysis | Spatial cluster analysis uses geographically referenced observations and is a subset of cluster analysis that is not limited to exploratory analysis.
Example 1
It can be used to make fair election districts.
Example 2
Local spatial autocorrelation measures are used in the AMOEBA method of clustering. Aldstadt and Geti... | Assumptions of cluster analysis | Spatial cluster analysis uses geographically referenced observations and is a subset of cluster analysis that is not limited to exploratory analysis.
Example 1
It can be used to make fair election di | Assumptions of cluster analysis
Spatial cluster analysis uses geographically referenced observations and is a subset of cluster analysis that is not limited to exploratory analysis.
Example 1
It can be used to make fair election districts.
Example 2
Local spatial autocorrelation measures are used in the AMOEBA method ... | Assumptions of cluster analysis
Spatial cluster analysis uses geographically referenced observations and is a subset of cluster analysis that is not limited to exploratory analysis.
Example 1
It can be used to make fair election di |
15,768 | Assumptions of cluster analysis | Cluster analysis does not involve hypothesis testing per se, but is really just a collection of different similarity algorithms for exploratory analysis. You can force hypothesis testing somewhat but the results are often inconsistent, since cluster changes are very sensitive to changes in parameters.
http://support.sa... | Assumptions of cluster analysis | Cluster analysis does not involve hypothesis testing per se, but is really just a collection of different similarity algorithms for exploratory analysis. You can force hypothesis testing somewhat but | Assumptions of cluster analysis
Cluster analysis does not involve hypothesis testing per se, but is really just a collection of different similarity algorithms for exploratory analysis. You can force hypothesis testing somewhat but the results are often inconsistent, since cluster changes are very sensitive to changes ... | Assumptions of cluster analysis
Cluster analysis does not involve hypothesis testing per se, but is really just a collection of different similarity algorithms for exploratory analysis. You can force hypothesis testing somewhat but |
15,769 | If an auto-regressive time series model is non-linear, does it still require stationarity? | If the purpose of your model is prediction and forecasting, then the short answer is YES, but the stationarity doesn't need to be on levels.
I'll explain. If you boil down forecasting to its most basic form, it's going to be extraction of the invariant. Consider this: you cannot forecast what's changing. If I tell you ... | If an auto-regressive time series model is non-linear, does it still require stationarity? | If the purpose of your model is prediction and forecasting, then the short answer is YES, but the stationarity doesn't need to be on levels.
I'll explain. If you boil down forecasting to its most basi | If an auto-regressive time series model is non-linear, does it still require stationarity?
If the purpose of your model is prediction and forecasting, then the short answer is YES, but the stationarity doesn't need to be on levels.
I'll explain. If you boil down forecasting to its most basic form, it's going to be extr... | If an auto-regressive time series model is non-linear, does it still require stationarity?
If the purpose of your model is prediction and forecasting, then the short answer is YES, but the stationarity doesn't need to be on levels.
I'll explain. If you boil down forecasting to its most basi |
15,770 | Methods to work around the problem of missing data in machine learning | The technique you describe is called imputation by sequential regressions or multiple imputation by chained equations. The technique was pioneered by Raghunathan (2001) and implemented in a well working R package called mice (van Buuren, 2012).
A paper by Schafer and Graham (2002) explains well why mean imputation and... | Methods to work around the problem of missing data in machine learning | The technique you describe is called imputation by sequential regressions or multiple imputation by chained equations. The technique was pioneered by Raghunathan (2001) and implemented in a well worki | Methods to work around the problem of missing data in machine learning
The technique you describe is called imputation by sequential regressions or multiple imputation by chained equations. The technique was pioneered by Raghunathan (2001) and implemented in a well working R package called mice (van Buuren, 2012).
A p... | Methods to work around the problem of missing data in machine learning
The technique you describe is called imputation by sequential regressions or multiple imputation by chained equations. The technique was pioneered by Raghunathan (2001) and implemented in a well worki |
15,771 | Methods to work around the problem of missing data in machine learning | I did not find anything that solved my problem so I wrote a function that mixes some solutions to a Pandas dataframe with missing numerical values (with fancyimpute) and categorical (with a random forest).
import pandas as pd
import numpy as np
from sklearn.ensemble import RandomForestClassifier
import fancyimpute as f... | Methods to work around the problem of missing data in machine learning | I did not find anything that solved my problem so I wrote a function that mixes some solutions to a Pandas dataframe with missing numerical values (with fancyimpute) and categorical (with a random for | Methods to work around the problem of missing data in machine learning
I did not find anything that solved my problem so I wrote a function that mixes some solutions to a Pandas dataframe with missing numerical values (with fancyimpute) and categorical (with a random forest).
import pandas as pd
import numpy as np
from... | Methods to work around the problem of missing data in machine learning
I did not find anything that solved my problem so I wrote a function that mixes some solutions to a Pandas dataframe with missing numerical values (with fancyimpute) and categorical (with a random for |
15,772 | Methods to work around the problem of missing data in machine learning | Though typically more involved, you can try and created a Maximum Entropy Distribution based on what data you have.
http://proceedings.mlr.press/v5/huang09a/huang09a.pdf | Methods to work around the problem of missing data in machine learning | Though typically more involved, you can try and created a Maximum Entropy Distribution based on what data you have.
http://proceedings.mlr.press/v5/huang09a/huang09a.pdf | Methods to work around the problem of missing data in machine learning
Though typically more involved, you can try and created a Maximum Entropy Distribution based on what data you have.
http://proceedings.mlr.press/v5/huang09a/huang09a.pdf | Methods to work around the problem of missing data in machine learning
Though typically more involved, you can try and created a Maximum Entropy Distribution based on what data you have.
http://proceedings.mlr.press/v5/huang09a/huang09a.pdf |
15,773 | How to respond to reviewers asking for p-values in bayesian multilevel model? | First, a quick clarification: Although the likelihood is indeed not the posterior, p-values are not so much inconsistent with Bayesian inference as usually just a different thing, for all the reasons that confidence intervals may or may not line up with credible intervals. (Although not necessarily an entirely differe... | How to respond to reviewers asking for p-values in bayesian multilevel model? | First, a quick clarification: Although the likelihood is indeed not the posterior, p-values are not so much inconsistent with Bayesian inference as usually just a different thing, for all the reasons | How to respond to reviewers asking for p-values in bayesian multilevel model?
First, a quick clarification: Although the likelihood is indeed not the posterior, p-values are not so much inconsistent with Bayesian inference as usually just a different thing, for all the reasons that confidence intervals may or may not l... | How to respond to reviewers asking for p-values in bayesian multilevel model?
First, a quick clarification: Although the likelihood is indeed not the posterior, p-values are not so much inconsistent with Bayesian inference as usually just a different thing, for all the reasons |
15,774 | How to explain hypothesis testing for teenagers in less than 10 minutes? | I think you should start with asking them what they think it really means to say about a person that he or she is able to tell the difference between coca-cola and pepsi. What can such a person do that others can not do?
Most of them will not have any such definition, and will not be able to produce one if asked. Howev... | How to explain hypothesis testing for teenagers in less than 10 minutes? | I think you should start with asking them what they think it really means to say about a person that he or she is able to tell the difference between coca-cola and pepsi. What can such a person do tha | How to explain hypothesis testing for teenagers in less than 10 minutes?
I think you should start with asking them what they think it really means to say about a person that he or she is able to tell the difference between coca-cola and pepsi. What can such a person do that others can not do?
Most of them will not have... | How to explain hypothesis testing for teenagers in less than 10 minutes?
I think you should start with asking them what they think it really means to say about a person that he or she is able to tell the difference between coca-cola and pepsi. What can such a person do tha |
15,775 | How to explain hypothesis testing for teenagers in less than 10 minutes? | Working with soda sounds fun, and the test of whether teenagers can actually tell the difference between sodas makes sense once you have a reasonable knowledge of hypothesis testing. The problem might be that this question: "can you actually tell the difference between sodas?" is complicated by lots of other stuff in t... | How to explain hypothesis testing for teenagers in less than 10 minutes? | Working with soda sounds fun, and the test of whether teenagers can actually tell the difference between sodas makes sense once you have a reasonable knowledge of hypothesis testing. The problem might | How to explain hypothesis testing for teenagers in less than 10 minutes?
Working with soda sounds fun, and the test of whether teenagers can actually tell the difference between sodas makes sense once you have a reasonable knowledge of hypothesis testing. The problem might be that this question: "can you actually tell ... | How to explain hypothesis testing for teenagers in less than 10 minutes?
Working with soda sounds fun, and the test of whether teenagers can actually tell the difference between sodas makes sense once you have a reasonable knowledge of hypothesis testing. The problem might |
15,776 | How to explain hypothesis testing for teenagers in less than 10 minutes? | Consider someone doing target practice with a shotgun, which shoots bursts of pellets in the direction of the barrel.
Null Hypothesis: I'm a good shooter, and my barrel is perfectly on target. Not left, not right, but straight at it. My error is 0.
Alternate Hypothesis: I'm a bad shooter, and my barrel is off target. J... | How to explain hypothesis testing for teenagers in less than 10 minutes? | Consider someone doing target practice with a shotgun, which shoots bursts of pellets in the direction of the barrel.
Null Hypothesis: I'm a good shooter, and my barrel is perfectly on target. Not lef | How to explain hypothesis testing for teenagers in less than 10 minutes?
Consider someone doing target practice with a shotgun, which shoots bursts of pellets in the direction of the barrel.
Null Hypothesis: I'm a good shooter, and my barrel is perfectly on target. Not left, not right, but straight at it. My error is 0... | How to explain hypothesis testing for teenagers in less than 10 minutes?
Consider someone doing target practice with a shotgun, which shoots bursts of pellets in the direction of the barrel.
Null Hypothesis: I'm a good shooter, and my barrel is perfectly on target. Not lef |
15,777 | How to explain hypothesis testing for teenagers in less than 10 minutes? | Assume the kids can't tell the difference and decide by chance. Then each kid has a 50% chance of guessing it right. So you expect (expected value) that in this case, 5 kids do it right and 5 kids err. Of course, as it is by chance, it is also possible that 6 kids err and 4 get it right, and so on. On the opposite side... | How to explain hypothesis testing for teenagers in less than 10 minutes? | Assume the kids can't tell the difference and decide by chance. Then each kid has a 50% chance of guessing it right. So you expect (expected value) that in this case, 5 kids do it right and 5 kids err | How to explain hypothesis testing for teenagers in less than 10 minutes?
Assume the kids can't tell the difference and decide by chance. Then each kid has a 50% chance of guessing it right. So you expect (expected value) that in this case, 5 kids do it right and 5 kids err. Of course, as it is by chance, it is also pos... | How to explain hypothesis testing for teenagers in less than 10 minutes?
Assume the kids can't tell the difference and decide by chance. Then each kid has a 50% chance of guessing it right. So you expect (expected value) that in this case, 5 kids do it right and 5 kids err |
15,778 | How to explain hypothesis testing for teenagers in less than 10 minutes? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
Show this video which is the most intuitive explanatio... | How to explain hypothesis testing for teenagers in less than 10 minutes? | Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
| How to explain hypothesis testing for teenagers in less than 10 minutes?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
... | How to explain hypothesis testing for teenagers in less than 10 minutes?
Want to improve this post? Provide detailed answers to this question, including citations and an explanation of why your answer is correct. Answers without enough detail may be edited or deleted.
|
15,779 | How to explain hypothesis testing for teenagers in less than 10 minutes? | The children tasting coke experiment is a good example to introduce hypothesis testing, as its equivalent the lady tasting tea experiment showed. However, evaluating those experiments is not very intuitive because the null hypothesis involves the binomial distribution with p=0.5, and it is not straightforward.
In my us... | How to explain hypothesis testing for teenagers in less than 10 minutes? | The children tasting coke experiment is a good example to introduce hypothesis testing, as its equivalent the lady tasting tea experiment showed. However, evaluating those experiments is not very intu | How to explain hypothesis testing for teenagers in less than 10 minutes?
The children tasting coke experiment is a good example to introduce hypothesis testing, as its equivalent the lady tasting tea experiment showed. However, evaluating those experiments is not very intuitive because the null hypothesis involves the ... | How to explain hypothesis testing for teenagers in less than 10 minutes?
The children tasting coke experiment is a good example to introduce hypothesis testing, as its equivalent the lady tasting tea experiment showed. However, evaluating those experiments is not very intu |
15,780 | Introductory texts on structural econometrics | I am not aware of anything like this. Paarsch and Hong's An Introduction to the Structural Econometrics of Auction Data and Ada and Cooper's Dynamic Economics come closest.
The usual classroom approach is to read classic papers and perhaps replicate one along the way. Here's one example (Jean-Marc Robin). Here's are m... | Introductory texts on structural econometrics | I am not aware of anything like this. Paarsch and Hong's An Introduction to the Structural Econometrics of Auction Data and Ada and Cooper's Dynamic Economics come closest.
The usual classroom approa | Introductory texts on structural econometrics
I am not aware of anything like this. Paarsch and Hong's An Introduction to the Structural Econometrics of Auction Data and Ada and Cooper's Dynamic Economics come closest.
The usual classroom approach is to read classic papers and perhaps replicate one along the way. Here... | Introductory texts on structural econometrics
I am not aware of anything like this. Paarsch and Hong's An Introduction to the Structural Econometrics of Auction Data and Ada and Cooper's Dynamic Economics come closest.
The usual classroom approa |
15,781 | Introductory texts on structural econometrics | If you also consider structural methods for macroeconomics then perhaps the book Structural Macroeconometrics by DeJong and Dave will be interesting.
Mathias Andre has some structural econometrics problem sets on his website. The questions are similar to the example in chapter one of Wolpin's book you mentioned in the ... | Introductory texts on structural econometrics | If you also consider structural methods for macroeconomics then perhaps the book Structural Macroeconometrics by DeJong and Dave will be interesting.
Mathias Andre has some structural econometrics pro | Introductory texts on structural econometrics
If you also consider structural methods for macroeconomics then perhaps the book Structural Macroeconometrics by DeJong and Dave will be interesting.
Mathias Andre has some structural econometrics problem sets on his website. The questions are similar to the example in chap... | Introductory texts on structural econometrics
If you also consider structural methods for macroeconomics then perhaps the book Structural Macroeconometrics by DeJong and Dave will be interesting.
Mathias Andre has some structural econometrics pro |
15,782 | Introductory texts on structural econometrics | One problem is that structural estimation varies a lot depending on what field you are in, as the models used vary a lot. To me, at least, structural estimation in labor and marketing looks wildly different from finance and macro. It might be helpful to separate estimation methods (method of simulated moments, simulate... | Introductory texts on structural econometrics | One problem is that structural estimation varies a lot depending on what field you are in, as the models used vary a lot. To me, at least, structural estimation in labor and marketing looks wildly dif | Introductory texts on structural econometrics
One problem is that structural estimation varies a lot depending on what field you are in, as the models used vary a lot. To me, at least, structural estimation in labor and marketing looks wildly different from finance and macro. It might be helpful to separate estimation ... | Introductory texts on structural econometrics
One problem is that structural estimation varies a lot depending on what field you are in, as the models used vary a lot. To me, at least, structural estimation in labor and marketing looks wildly dif |
15,783 | Brant test in R [closed] | I implemented the brant test in R. The package and function is called brant and it's now available on CRAN.
The brant test was defined by Rollin Brant to test the parallel regression assumption (Brant, R. (1990) Assessing proportionality in the proportional odds model for ordinal logistic regression. Biometrics, 46, 1... | Brant test in R [closed] | I implemented the brant test in R. The package and function is called brant and it's now available on CRAN.
The brant test was defined by Rollin Brant to test the parallel regression assumption (Bran | Brant test in R [closed]
I implemented the brant test in R. The package and function is called brant and it's now available on CRAN.
The brant test was defined by Rollin Brant to test the parallel regression assumption (Brant, R. (1990) Assessing proportionality in the proportional odds model for ordinal logistic regr... | Brant test in R [closed]
I implemented the brant test in R. The package and function is called brant and it's now available on CRAN.
The brant test was defined by Rollin Brant to test the parallel regression assumption (Bran |
15,784 | Brant test in R [closed] | Yes -- in fact the ordinal package that you linked can do it (although they don't call it the Brant test). Take a look at pages 6 and 7 of your link, which demonstrate "a likelihood ratio test of the equal slopes or proportional odds assumption," which is exactly what you are looking for. | Brant test in R [closed] | Yes -- in fact the ordinal package that you linked can do it (although they don't call it the Brant test). Take a look at pages 6 and 7 of your link, which demonstrate "a likelihood ratio test of the | Brant test in R [closed]
Yes -- in fact the ordinal package that you linked can do it (although they don't call it the Brant test). Take a look at pages 6 and 7 of your link, which demonstrate "a likelihood ratio test of the equal slopes or proportional odds assumption," which is exactly what you are looking for. | Brant test in R [closed]
Yes -- in fact the ordinal package that you linked can do it (although they don't call it the Brant test). Take a look at pages 6 and 7 of your link, which demonstrate "a likelihood ratio test of the |
15,785 | Brant test in R [closed] | Some notes on the topic
The R package VGAM in the cumulative command (Ordinal Regression with Cumulative Probabilities) allows to change the proportional odds assumptions, with the option parallel=FALSE.
It is known to be a common problem (from the book: Regression Models for Categorical Dependent Variables Using Stata... | Brant test in R [closed] | Some notes on the topic
The R package VGAM in the cumulative command (Ordinal Regression with Cumulative Probabilities) allows to change the proportional odds assumptions, with the option parallel=FAL | Brant test in R [closed]
Some notes on the topic
The R package VGAM in the cumulative command (Ordinal Regression with Cumulative Probabilities) allows to change the proportional odds assumptions, with the option parallel=FALSE.
It is known to be a common problem (from the book: Regression Models for Categorical Depend... | Brant test in R [closed]
Some notes on the topic
The R package VGAM in the cumulative command (Ordinal Regression with Cumulative Probabilities) allows to change the proportional odds assumptions, with the option parallel=FAL |
15,786 | Brant test in R [closed] | This tutorial about ordinal logistic regression in R covers testing the proportional odds assumption. | Brant test in R [closed] | This tutorial about ordinal logistic regression in R covers testing the proportional odds assumption. | Brant test in R [closed]
This tutorial about ordinal logistic regression in R covers testing the proportional odds assumption. | Brant test in R [closed]
This tutorial about ordinal logistic regression in R covers testing the proportional odds assumption. |
15,787 | Alternative to sieve / mosaic plots for contingency tables | The book you described sounds like, 'Visualizing Categorical Data,' Michael Friendly.
The plot described in the 1st chapter that seems to match your request was described as a type of conceptual model for visualizing contingency table data (loosely described by the author as a dynamic pressure model with observational ... | Alternative to sieve / mosaic plots for contingency tables | The book you described sounds like, 'Visualizing Categorical Data,' Michael Friendly.
The plot described in the 1st chapter that seems to match your request was described as a type of conceptual model | Alternative to sieve / mosaic plots for contingency tables
The book you described sounds like, 'Visualizing Categorical Data,' Michael Friendly.
The plot described in the 1st chapter that seems to match your request was described as a type of conceptual model for visualizing contingency table data (loosely described by... | Alternative to sieve / mosaic plots for contingency tables
The book you described sounds like, 'Visualizing Categorical Data,' Michael Friendly.
The plot described in the 1st chapter that seems to match your request was described as a type of conceptual model |
15,788 | Alternative to sieve / mosaic plots for contingency tables | Maybe not what you saw, but for visualization of departures expected under independence
correspondence plots are well motivated.
http://www.jstatsoft.org/v20/i03/
(An an aside, SAS and M Friendly's book were mistaken about the recommended adjustment and many of the plots had artifacts in them and this may have distra... | Alternative to sieve / mosaic plots for contingency tables | Maybe not what you saw, but for visualization of departures expected under independence
correspondence plots are well motivated.
http://www.jstatsoft.org/v20/i03/
(An an aside, SAS and M Friendly's | Alternative to sieve / mosaic plots for contingency tables
Maybe not what you saw, but for visualization of departures expected under independence
correspondence plots are well motivated.
http://www.jstatsoft.org/v20/i03/
(An an aside, SAS and M Friendly's book were mistaken about the recommended adjustment and many ... | Alternative to sieve / mosaic plots for contingency tables
Maybe not what you saw, but for visualization of departures expected under independence
correspondence plots are well motivated.
http://www.jstatsoft.org/v20/i03/
(An an aside, SAS and M Friendly's |
15,789 | How to do one-class text classification? | The Spy EM algorithm solves exactly this problem.
S-EM is a text learning or classification system that learns from a set of positive and unlabeled examples (no negative examples). It is based on a "spy" technique, naive Bayes and EM algorithm.
The basic idea is to combine your positive set with a whole bunch of rand... | How to do one-class text classification? | The Spy EM algorithm solves exactly this problem.
S-EM is a text learning or classification system that learns from a set of positive and unlabeled examples (no negative examples). It is based on a " | How to do one-class text classification?
The Spy EM algorithm solves exactly this problem.
S-EM is a text learning or classification system that learns from a set of positive and unlabeled examples (no negative examples). It is based on a "spy" technique, naive Bayes and EM algorithm.
The basic idea is to combine you... | How to do one-class text classification?
The Spy EM algorithm solves exactly this problem.
S-EM is a text learning or classification system that learns from a set of positive and unlabeled examples (no negative examples). It is based on a " |
15,790 | How to do one-class text classification? | Here is a good thesis about one-class classification:
Tax, D. M.: One-class classification - Concept-learning in the absence of counter-examples, PhD thesis, Technische Universiteit Delft, 2001. (pdf)
This thesis introduces the method of Support Vector Data Description (SVDD), a one-class support vector machine tha... | How to do one-class text classification? | Here is a good thesis about one-class classification:
Tax, D. M.: One-class classification - Concept-learning in the absence of counter-examples, PhD thesis, Technische Universiteit Delft, 2001. (p | How to do one-class text classification?
Here is a good thesis about one-class classification:
Tax, D. M.: One-class classification - Concept-learning in the absence of counter-examples, PhD thesis, Technische Universiteit Delft, 2001. (pdf)
This thesis introduces the method of Support Vector Data Description (SVDD... | How to do one-class text classification?
Here is a good thesis about one-class classification:
Tax, D. M.: One-class classification - Concept-learning in the absence of counter-examples, PhD thesis, Technische Universiteit Delft, 2001. (p |
15,791 | How to do one-class text classification? | Good training requires data that provides good estimates of the individual class probabilities. Every classification problem involves at least two classes. In your case the second class is anyone that is not in the positive class. To form a good decision boundary using Bayes or any other good method is best done wit... | How to do one-class text classification? | Good training requires data that provides good estimates of the individual class probabilities. Every classification problem involves at least two classes. In your case the second class is anyone th | How to do one-class text classification?
Good training requires data that provides good estimates of the individual class probabilities. Every classification problem involves at least two classes. In your case the second class is anyone that is not in the positive class. To form a good decision boundary using Bayes ... | How to do one-class text classification?
Good training requires data that provides good estimates of the individual class probabilities. Every classification problem involves at least two classes. In your case the second class is anyone th |
15,792 | How to do one-class text classification? | I agree with Michael.
Regarding your question about random selection; yes: you have to select randomly from the complementary set of your 'positives'. If there is any confusion that it is possible that your 'positives' are not fully defined as 'pure positive', if I may use that phrase, then you can also try at the ... | How to do one-class text classification? | I agree with Michael.
Regarding your question about random selection; yes: you have to select randomly from the complementary set of your 'positives'. If there is any confusion that it is possible | How to do one-class text classification?
I agree with Michael.
Regarding your question about random selection; yes: you have to select randomly from the complementary set of your 'positives'. If there is any confusion that it is possible that your 'positives' are not fully defined as 'pure positive', if I may use t... | How to do one-class text classification?
I agree with Michael.
Regarding your question about random selection; yes: you have to select randomly from the complementary set of your 'positives'. If there is any confusion that it is possible |
15,793 | How to do one-class text classification? | An article that may be of interest is:
"Extended nearest shrunken centroid classification: A new method for
open-set authorship attribution of texts of varying sizes", Schaalje,
Fields, Roper, and Snow. Literary and Linguistic Computing, vol. 26,
No. 1, 2011.
Which takes a method for attributing a text to a se... | How to do one-class text classification? | An article that may be of interest is:
"Extended nearest shrunken centroid classification: A new method for
open-set authorship attribution of texts of varying sizes", Schaalje,
Fields, Roper, an | How to do one-class text classification?
An article that may be of interest is:
"Extended nearest shrunken centroid classification: A new method for
open-set authorship attribution of texts of varying sizes", Schaalje,
Fields, Roper, and Snow. Literary and Linguistic Computing, vol. 26,
No. 1, 2011.
Which take... | How to do one-class text classification?
An article that may be of interest is:
"Extended nearest shrunken centroid classification: A new method for
open-set authorship attribution of texts of varying sizes", Schaalje,
Fields, Roper, an |
15,794 | Logistic Regression and Dataset Structure | Do a logistic regression with covariates "play time" and "goals(home team) - goals(away team)". You will need an interaction effect of these terms since a 2 goal lead at half-time will have a much smaller effect than a 2 goal lead with only 1 minute left. Your response is "victory (home team)".
Don't just assume linea... | Logistic Regression and Dataset Structure | Do a logistic regression with covariates "play time" and "goals(home team) - goals(away team)". You will need an interaction effect of these terms since a 2 goal lead at half-time will have a much sma | Logistic Regression and Dataset Structure
Do a logistic regression with covariates "play time" and "goals(home team) - goals(away team)". You will need an interaction effect of these terms since a 2 goal lead at half-time will have a much smaller effect than a 2 goal lead with only 1 minute left. Your response is "vict... | Logistic Regression and Dataset Structure
Do a logistic regression with covariates "play time" and "goals(home team) - goals(away team)". You will need an interaction effect of these terms since a 2 goal lead at half-time will have a much sma |
15,795 | Logistic Regression and Dataset Structure | I would start simulating the data from a toy model. Something like:
n.games <- 1000
n.slices <- 90
score.away <- score.home <- matrix(0, ncol=n.slices, nrow=n.games)
for (j in 2:n.slices) {
score.home[ ,j] <- score.home[ , j-1] + (runif(n.games)>.97)
score.away[ ,j] <- score.away[ , j-1] + (runif(n.games)>.98)
}... | Logistic Regression and Dataset Structure | I would start simulating the data from a toy model. Something like:
n.games <- 1000
n.slices <- 90
score.away <- score.home <- matrix(0, ncol=n.slices, nrow=n.games)
for (j in 2:n.slices) {
score | Logistic Regression and Dataset Structure
I would start simulating the data from a toy model. Something like:
n.games <- 1000
n.slices <- 90
score.away <- score.home <- matrix(0, ncol=n.slices, nrow=n.games)
for (j in 2:n.slices) {
score.home[ ,j] <- score.home[ , j-1] + (runif(n.games)>.97)
score.away[ ,j] <- s... | Logistic Regression and Dataset Structure
I would start simulating the data from a toy model. Something like:
n.games <- 1000
n.slices <- 90
score.away <- score.home <- matrix(0, ncol=n.slices, nrow=n.games)
for (j in 2:n.slices) {
score |
15,796 | Logistic Regression and Dataset Structure | Check out the stats nerds at Football Outsiders as well as the book Mathletics for some inspiration.
The Football Outsiders guys make game predictions based on every play in a football game.
Winston in Mathletics uses some techniques such as dynamic programming as well.
You can also consider other algorithms such as ... | Logistic Regression and Dataset Structure | Check out the stats nerds at Football Outsiders as well as the book Mathletics for some inspiration.
The Football Outsiders guys make game predictions based on every play in a football game.
Winston | Logistic Regression and Dataset Structure
Check out the stats nerds at Football Outsiders as well as the book Mathletics for some inspiration.
The Football Outsiders guys make game predictions based on every play in a football game.
Winston in Mathletics uses some techniques such as dynamic programming as well.
You c... | Logistic Regression and Dataset Structure
Check out the stats nerds at Football Outsiders as well as the book Mathletics for some inspiration.
The Football Outsiders guys make game predictions based on every play in a football game.
Winston |
15,797 | LASSO assumptions | I am not an expert on LASSO, but here is my take.
First note that OLS is pretty robust to violations of indepence and normality. Then judging from the Theorem 7 and the discussion above it in the article Robust Regression and Lasso (by X. Huan, C. Caramanis and S. Mannor) I guess, that in LASSO regression we are more... | LASSO assumptions | I am not an expert on LASSO, but here is my take.
First note that OLS is pretty robust to violations of indepence and normality. Then judging from the Theorem 7 and the discussion above it in the ar | LASSO assumptions
I am not an expert on LASSO, but here is my take.
First note that OLS is pretty robust to violations of indepence and normality. Then judging from the Theorem 7 and the discussion above it in the article Robust Regression and Lasso (by X. Huan, C. Caramanis and S. Mannor) I guess, that in LASSO regr... | LASSO assumptions
I am not an expert on LASSO, but here is my take.
First note that OLS is pretty robust to violations of indepence and normality. Then judging from the Theorem 7 and the discussion above it in the ar |
15,798 | Why do we call the equations of least square estimation in linear regression the *normal equations*? | I'll give what is perhaps the most common understanding, then some additional details.
Normal is a term in geometry (Wikipedia):
In geometry, a normal is an object such as a line or vector that is perpendicular to a given object.
which in turn appears to come from a term for a carpenter's or mason's square [1]
NORM ... | Why do we call the equations of least square estimation in linear regression the *normal equations*? | I'll give what is perhaps the most common understanding, then some additional details.
Normal is a term in geometry (Wikipedia):
In geometry, a normal is an object such as a line or vector that is pe | Why do we call the equations of least square estimation in linear regression the *normal equations*?
I'll give what is perhaps the most common understanding, then some additional details.
Normal is a term in geometry (Wikipedia):
In geometry, a normal is an object such as a line or vector that is perpendicular to a gi... | Why do we call the equations of least square estimation in linear regression the *normal equations*?
I'll give what is perhaps the most common understanding, then some additional details.
Normal is a term in geometry (Wikipedia):
In geometry, a normal is an object such as a line or vector that is pe |
15,799 | 2SLS but second stage Probit | Your case is less problematic than the other way round. The expectations and linear projections operators go through a linear first stage (e.g. OLS) but not not through non-linear ones like probit or logit. Therefore it's not a problem if you first regress your continous endogenous variable $X$ on your instrument(s) $Z... | 2SLS but second stage Probit | Your case is less problematic than the other way round. The expectations and linear projections operators go through a linear first stage (e.g. OLS) but not not through non-linear ones like probit or | 2SLS but second stage Probit
Your case is less problematic than the other way round. The expectations and linear projections operators go through a linear first stage (e.g. OLS) but not not through non-linear ones like probit or logit. Therefore it's not a problem if you first regress your continous endogenous variable... | 2SLS but second stage Probit
Your case is less problematic than the other way round. The expectations and linear projections operators go through a linear first stage (e.g. OLS) but not not through non-linear ones like probit or |
15,800 | How to perform residual analysis for binary/dichotomous independent predictors in linear regression? | @NickCox has done a good job talking about displays of residuals when you have two groups. Let me address some of the explicit questions and implicit assumptions that lie behind this thread.
The question asks, "how do you test assumptions of linear regression such as homoscedasticity when an independent variable is ... | How to perform residual analysis for binary/dichotomous independent predictors in linear regression? | @NickCox has done a good job talking about displays of residuals when you have two groups. Let me address some of the explicit questions and implicit assumptions that lie behind this thread.
The qu | How to perform residual analysis for binary/dichotomous independent predictors in linear regression?
@NickCox has done a good job talking about displays of residuals when you have two groups. Let me address some of the explicit questions and implicit assumptions that lie behind this thread.
The question asks, "how d... | How to perform residual analysis for binary/dichotomous independent predictors in linear regression?
@NickCox has done a good job talking about displays of residuals when you have two groups. Let me address some of the explicit questions and implicit assumptions that lie behind this thread.
The qu |
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