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1500 questions
36
votes
1 answer
How to interpret variance and correlation of random effects in a mixed-effects model?
I hope you all don't mind this question, but I need help interpreting output for a linear mixed effects model output I've been trying to learn to do in R. I am new to longitudinal data analysis and linear mixed effects regression. I have a model I…
Zeda
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36
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2 answers
Can you explain Parzen window (kernel) density estimation in layman's terms?
Parzen window density estimation is described as
$$ p(x)=\frac{1}{n}\sum_{i=1}^{n} \frac{1}{h^2} \phi \left(\frac{x_i - x}{h} \right) $$
where $n$ is number of elements in the vector, $x$ is a vector, $p(x)$ is a probability density of $x$, $h$ is…
user366312
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36
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7 answers
Are there algorithms for computing "running" linear or logistic regression parameters?
A paper "Accurately computing running variance" at http://www.johndcook.com/standard_deviation.html
shows how to compute running mean, variance and standard deviations.
Are there algorithms where the parameters of a linear or logistic regression…
adrcuth
36
votes
2 answers
Is Tikhonov regularization the same as Ridge Regression?
Tikhonov regularization and ridge regression are terms often used as if they were identical. Is it possible to specify exactly what the difference is?
Carl
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36
votes
3 answers
How to draw neat polygons around scatterplot regions in ggplot2
How do I add a neat polygon around a group of points on a scatterplot? I am using ggplot2 but am disappointed with the results of geom_polygon.
The dataset is over there, as a tab-delimited text file. The graph below shows two measures of attitudes…
Fr.
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36
votes
2 answers
Bootstrap prediction interval
Is there any bootstrap technique available to compute prediction intervals for point predictions obtained e.g. from linear regression or other regression method (k-nearest neighbour, regression trees etc.)?
Somehow I feel that the sometimes proposed…
Michael M
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36
votes
5 answers
Why use regularisation in polynomial regression instead of lowering the degree?
When doing regression, for example, two hyper parameters to choose are often the capacity of the function (eg. the largest exponent of a polynomial), and the amount of regularisation. What I'm confused about, is why not just choose a low capacity…
Karnivaurus
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36
votes
1 answer
Cross-validation misuse (reporting performance for the best hyperparameter value)
Recently I have come across a paper that proposes using a k-NN classifier on an specific dataset. The authors used all the data samples available to perform k-fold cross validation for different k values and report cross validation results of the…
Daniel López
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36
votes
3 answers
Why is the mean function in Gaussian Process uninteresting?
I have just started reading about GPs and analogous to the regular Gaussian distribution it is characterized by a mean function and the covariance function or the kernel. I was at a talk and the speaker said that the mean function is usually quite…
Luca
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36
votes
3 answers
Computing p-value using bootstrap with R
I use "boot" package to compute an approximated 2-sided bootstrapped p-value but the result is too far away from p-value of using t.test. I can't figure out what I did wrong in my R code. Can someone please give me a hint for this
time =…
Tu.2
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36
votes
2 answers
Is there a reliable nonparametric confidence interval for the mean of a skewed distribution?
Very skewed distributions such as the log-normal do not result in accurate bootstrap confidence intervals. Here is an example showing that the left and right tail areas are far from the ideal 0.025 no matter which bootstrap method you try in…
Frank Harrell
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36
votes
2 answers
Is this the state of art regression methodology?
I've been following Kaggle competitions for a long time and I come to realize that many winning strategies involve using at least one of the "big threes": bagging, boosting and stacking.
For regressions, rather than focusing on building one best…
Maxareo
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36
votes
3 answers
How to fit an ARIMAX-model with R?
I have four different time series of hourly measurements:
The heat consumption inside a house
The temperature outside the house
The solar radiation
The wind speed
I want to be able to predict the heat consumption inside the house. There is a clear…
utdiscant
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36
votes
1 answer
Bootstrapping vs Bayesian Bootstrapping conceptually?
I'm having a trouble understanding what a Bayesian Bootstrapping process is, and how that would differ from your normal bootstrapping. And if someone could offer an intuitive/conceptual review and comparison of both, that would be great.
Let's take…
SpicyClubSauce
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36
votes
3 answers
Pre-training in deep convolutional neural network?
Have anyone seen any literature on pre-training in deep convolutional neural network? I have only seen unsupervised pre-training in autoencoder or restricted boltzman machines.
RockTheStar
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