文章基本信息

标题：Bayesian Model Averaging Employing Fixed and Flexible Priors: The BMS Package for R
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作者：Stefan Zeugner ; Martin Feldkircher
期刊名称：Journal of Statistical Software
印刷版ISSN：1548-7660
电子版ISSN：1548-7660
出版年度：2015
卷号：68
期号：1
页码：1-37
语种：English
出版社：University of California, Los Angeles
摘要：This article describes the BMS (Bayesian model sampling) package for R that implements Bayesian model averaging for linear regression models. The package excels in allowing for a variety of prior structures, among them the "binomial-beta" prior on the model space and the so-called "hyper-g" specifications for Zellner's g prior. Furthermore, the BMS package allows the user to specify her own model priors and offers a possibility of subjective inference by setting "prior inclusion probabilities" according to the researcher's beliefs. Furthermore, graphical analysis of results is provided by numerous built-in plot functions of posterior densities, predictive densities and graphical illustrations to compare results under different prior settings. Finally, the package provides full enumeration of the model space for small scale problems as well as two efficient MCMC (Markov chain Monte Carlo) samplers that sort through the model space when the number of potential covariates is large.
关键词：hyper-g prior;binomial-beta prior;empirical Bayes;customized prior inclusion probabilities;BMS;R