Bayesian variable selection in binary quantile regression

Man Suk Oh, Eun Sug Park, Beong Soo So

Research output: Contribution to journalArticlepeer-review

7 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


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


Dive into the research topics of 'Bayesian variable selection in binary quantile regression'. Together they form a unique fingerprint.

Cite this