Posts tagged linear model

Heteroscedastic Bayesian Robust Regression

The PyMC gallery has two robust regression notebooks: one with a Student-t likelihood (pymc-examples:GLM-robust) and one with the Hogg (2010) signal-vs-noise mixture (pymc-examples:GLM-robust-with-outlier-detection). Both protect against vertical outliers (points with unusual response values), but neither defends against leverage points: observations far from the bulk of the predictor space, which can drag the regression line even under a heavy-tailed likelihood.

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Simpson’s paradox

Simpson’s Paradox describes a situation where there might be a negative relationship between two variables within a group, but when data from multiple groups are combined, that relationship may disappear or even reverse sign. The gif below (from the Simpson’s Paradox Wikipedia page) demonstrates this very nicely.

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Multivariate Gaussian Random Walk

This notebook shows how to fit a correlated time series using multivariate Gaussian random walks (GRWs). In particular, we perform a Bayesian regression of the time series data against a model dependent on GRWs.

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GLM: Robust Linear Regression

Duplicate implicit target name: “glm: robust linear regression”.

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