Methods and Tools for Bayesian Variable Selection and Model Averaging in Univariate Linear Regression

2016/12/07 by Anabel Forte, Forte, Anabel, Gonzalo García‐Donato +3
Mathematics · #Advanced Statistical Methods and Models #Computation (stat.CO) #FOS: Computer and information sciences #Statistical Methods and Bayesian Inference #Statistical Methods and Inference

paper · pdf · doi:10.48550/arxiv.1612.02357

openalex publication_date 2016/12/07 · openalex created_date 2019/07/30 · openalex updated_date 2026/07/28

Abstract

In this paper we briefly review the main methodological aspects concerned with the application of the Bayesian approach to model choice and model averaging in the context of variable selection in regression models. This includes prior elicitation, summaries of the posterior distribution and computational strategies. We then examine and compare various publicly available \tt R-packages for its practical implementation summarizing and explaining the differences between packages and giving recommendations for applied users. We find that all packages reviewed lead to very similar results, but there are potentially important differences in flexibility and efficiency of the packages.

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