Questions tagged [regression]
Techniques for analyzing the relationship between one (or more) "dependent" variables and "independent" variables.
9,665 questions with no upvoted or accepted answers
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Time series regression with overlapping data
I am seeing a regression model which is regressing Year-on-Year stock index returns on lagged (12 months) Year-on-Year returns of the same stock index, credit spread (difference between monthly mean ...
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Asymptotic property of tuning parameter in penalized regression
I'm currently working on asymptotic properties of penalized regression. I've read a myriad of papers by now, but there is an essential issue that I cannot get my head around.
To keep things simple, I'...
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Logistic regression for classification: are there any analytical solutions for the out-of-sample accuracy?
I run a binary logistic regression, with a binary dependent variable and a continuous independent one.
Now I want to evaluate the out-of-sample performance of the classification algorithm so obtained. ...
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Interpreting regression coefficients based on Andrew Gelman's re-scaling method
I have two predictors in a binary logistic regression model: One binary and one continuous. My primary goal is to compare the coefficients of the two predictors within the same model.
I have come ...
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Is autocorrelation not worth addressing with small N?
Consider a simple regression context in which there is a small set of response values, $Y$, and corresponding dates, $X$. (For simplicity, we can assume the dates are equally spaced.) We would like ...
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Pope effect on pizza - Regression with presence absence and similarity data as dependent variables
I'm trying to figure out the right way to set up a regression when the dependent variables are presence absence data (of pizzas), and the similarity between the present pizzas. Bear with the story:
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Bootstrap Prediction Interval: which residuals to use and which method?
I ask this question referring to the post: Bootstrap prediction interval, where a step by step method for calculating the prediction interval for linear regression models is explained.
In the ...
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When using L2 regularization outside of linear regression, do the same MAP estimation assumptions hold?
Some context is shared below, and my question is bolded at the end.
MLE from observation noise
In the linear regression setting, we learn model weights $\mathbf{w}$ to make scalar predictions $\hat{y}...
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How do sufficiency statistics help in the interpretation of regression results?
One of the results why canonical link functions are widely used in GLMs is the existence of sufficiency statistics for the regression parameters, which in turn allow for:
... minimal
sufficient ...
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What techniques are there to measure goodness of fit of Deming (orthogonal) regression?
Questions:
Even if there is no "widely accepted" technique, is there a useful-and-above-average technique for estimating goodness of fit in orthogonal regressions?
What are the pros/cons of this ...
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Errors-in-Variables model for logistic regression
Simple question: I am familiar (though don't have tons of experience) with errors-in-variables regression. From what I have seen, this mostly is used with continuous outcomes in a linear model.
A) Is ...
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Name for Generalized Generalized Linear Models
Consider the class of models given by $y\sim F(g^{-1}(\beta^\top\mathbf{x}))$ with $\mathbb{E}[Y]=g^{-1}(\beta^\top\mathbf{x})$.
Most authors I've come across call this a GLM only if F is in the ...
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What is difference between interrupted time series and regression discontinuity design
Say that one has data over time, t, on an outcome, y. There is an event that happens at t==0....
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T-test for regression coefficients obtained from Ridge, LASSO etc
In ordinary least squares, for example in an experimental design case, I obtain the regression coefficents by:
$ \hat B = {({X^t}{X})}^{-1}X^ty$
Then, my null hypothesis for each coefficent is:
$...
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Efficient nonparametric estimation of confidence intervals and p-values for nonlinear regression
I'm estimating parameters for a complex, "implicit" nonlinear model $f(\mathbf{x}, \boldsymbol{\theta})$. It's "implicit" in the sense that I don't have an explicit formula for $f$: its value is the ...
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