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자연과학대학
통계학전공
Journal papers
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Bayesian variable selection in binary quantile regression
Title
Bayesian variable selection in binary quantile regression
Authors
Oh M.-S.
;
Park E.S.
;
So B.-S.
Ewha Authors
소병수
;
오만숙
SCOPUS Author ID
소병수
; 오만숙
Issue Date
2016
Journal Title
Statistics and Probability Letters
ISSN
0167-7152
Citation
Statistics and Probability Letters vol. 118, pp. 177 - 181
Keywords
Bayes factor
;
Bayesian model selection
;
Markov chain Monte Carlo
;
Quantile regression
Publisher
Elsevier B.V.
Indexed
SCIE; SCOPUS
Document Type
Article
Abstract
We propose a simple Bayesian variable selection method in binary quantile regression. Our method computes the Bayes factors of all candidate models simultaneously based on a single set of MCMC samples from a model that encompasses all candidate models. The method deals with multicollinearity problems and variable selection under constraints. © 2016
DOI
10.1016/j.spl.2016.07.001
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