Bayesian variable selection in quantile regression using the Savage-Dickey density ratio
- 주제(키워드) Asymmetric Laplace distribution , Bayes factor , Bayesian model selection , Markov chain Monte Carlo , Primary , Secondary
- 등재 SCIE, KCI등재, SCOPUS
- 발행기관 Korean Statistical Society
- 발행년도 2016
- 총서유형 Journal
- URI http://www.dcollection.net/handler/ewha/000000125443
- 본문언어 영어
- Published As http://dx.doi.org/10.1016/j.jkss.2016.01.006
- 저작권 이화여자대학교 논문은 저작권에 의해 보호받습니다.
초록/요약
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. © 2016.
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