Bayesian variable selection in binary quantile regression

Man Suk Oh, Eun Sug Park, Beong Soo So

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


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 languageEnglish
Pages (from-to)177-181
Number of pages5
JournalStatistics and Probability Letters
StatePublished - 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


  • Bayes factor
  • Bayesian model selection
  • Markov chain Monte Carlo
  • Quantile regression


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