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

Title
Bayesian variable selection in quantile regression using the Savage-Dickey density ratio
Authors
Oh, Man-SukChoi, JungsoonPark, Eun Sug
Ewha Authors
오만숙
SCOPUS Author ID
오만숙scopus
Issue Date
2016
Journal Title
JOURNAL OF THE KOREAN STATISTICAL SOCIETY
ISSN
1226-3192JCR Link1876-4231JCR Link
Citation
vol. 45, no. 3, pp. 466 - 476
Keywords
Markov chain Monte CarloAsymmetric Laplace distributionBayesian model selectionBayes factor
Publisher
KOREAN STATISTICAL SOC
Indexed
SCIE; SCOPUS; KCI WOS
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. (C) 2016 Published by Elsevier B.V. on behalf of The Korean Statistical Society.
DOI
10.1016/j.kss.2016.01.006
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
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