This paper introduces a novel Bayesian approach to the problem of variable selection in high–dimensional logistic regression. In particular, we present a Marginalized Reversible Jump MCMC (MRJ) algorithm and its extensions, that exploits the data–augmentation structure using the Pólya–Gamma distribution. The proposed methods have been tested on simulated datasets, showing good perfomances in selecting the relevant regressors.
Bayesian Variable Selection for High Dimensional Logistic Regression
Claudio Busatto;Andrea Sottosanti;Mauro Bernardi
2019
Abstract
This paper introduces a novel Bayesian approach to the problem of variable selection in high–dimensional logistic regression. In particular, we present a Marginalized Reversible Jump MCMC (MRJ) algorithm and its extensions, that exploits the data–augmentation structure using the Pólya–Gamma distribution. The proposed methods have been tested on simulated datasets, showing good perfomances in selecting the relevant regressors.
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