Bayesian variable selection in binary quantile regression

Man Suk Oh; Eun Sug Park; Beong Soo So

doi:10.1016/j.spl.2016.07.001

Bayesian variable selection in binary quantile regression

Man Suk Oh, Eun Sug Park, Beong Soo So

Department of Statistics

Research output: Contribution to journal › Article › peer-review

8 Scopus citations

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.

Original language	English
Pages (from-to)	177-181
Number of pages	5
Journal	Statistics and Probability Letters
Volume	118
DOIs	https://doi.org/10.1016/j.spl.2016.07.001
State	Published - 1 Nov 2016

Bibliographical note

Funding Information:
This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology ( 2013R1A1A2005481 ).

Publisher Copyright:
© 2016

Keywords

Bayes factor
Bayesian model selection
Markov chain Monte Carlo
Quantile regression

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10.1016/j.spl.2016.07.001

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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.",

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author = "Oh, {Man Suk} and Park, {Eun Sug} and So, {Beong Soo}",

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KW - Bayesian model selection

KW - Markov chain Monte Carlo

KW - Quantile regression

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ER -

Bayesian variable selection in binary quantile regression

Abstract

Bibliographical note

Keywords

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