Bayesian variable selection in quantile regression using the Savage–Dickey density ratio

Man Suk Oh, Jungsoon Choi, Eun Sug Park

Research output: Contribution to journalArticlepeer-review

5 Scopus citations

Abstract

In this paper we propose a Bayesian variable selection method in quantile regression based on the Savage–Dickey density ratio of Dickey (1976). The Bayes factor of a model containing a subset of variables against an encompassing model is given as the ratio of the marginal posterior and the marginal prior density of the corresponding subset of regression coefficients under the encompassing model. Posterior samples are generated from the encompassing model via a Gibbs sampling algorithm and the Bayes factors of all candidate models are computed simultaneously using one set of posterior samples from the encompassing model. The performance of the proposed method is investigated via simulation examples and real data sets.

Original languageEnglish
Pages (from-to)466-476
Number of pages11
JournalJournal of the Korean Statistical Society
Volume45
Issue number3
DOIs
StatePublished - 1 Sep 2016

Keywords

  • Asymmetric Laplace distribution
  • Bayes factor
  • Bayesian model selection
  • Markov chain Monte Carlo

Fingerprint

Dive into the research topics of 'Bayesian variable selection in quantile regression using the Savage–Dickey density ratio'. Together they form a unique fingerprint.

Cite this