6533b857fe1ef96bd12b439a
RESEARCH PRODUCT
Methods and Tools for Bayesian Variable Selection and Model Averaging in Normal Linear Regression
Gonzalo García-donatoMark F. J. SteelAnabel Fortesubject
Statistics and ProbabilityGeneral linear modelProper linear modelbusiness.industryComputer science05 social sciencesPosterior probabilityRegression analysisFeature selectionMachine learningcomputer.software_genre01 natural sciences010104 statistics & probabilityBayesian multivariate linear regression0502 economics and businessLinear regressionEconometricsArtificial intelligence050207 economics0101 mathematicsStatistics Probability and UncertaintyBayesian linear regressionbusinesscomputerdescription
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 R-packages, summarizing and explaining the differences between packages and giving recommendations for applied users. We find that all packages reviewed (can) lead to very similar results, but there are potentially important differences in flexibility and efficiency of the packages.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-02-13 | International Statistical Review |