Bayesian variable selection in binary quantile regression Academic Article

abstract

• 2016 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.

authors

• Park, Eun

published proceedings

• STATISTICS & PROBABILITY LETTERS

author list (cited authors)

• Oh, M., Park, E. S., & So, B.

citation count

• 7

complete list of authors

• Oh, Man-Suk||Park, Eun Sug||So, Beong-Soo

publication date

• November 2016

publisher

• Elsevier  Publisher

published in

• Statistics and Probability Letters  Journal

keywords

• Bayes Factor
• Bayesian Model Selection
• Markov Chain Monte Carlo
• Quantile Regression

Digital Object Identifier (DOI)

• 10.1016/j.spl.2016.07.001

start page

• 177

end page

• 181

volume

• 118

URL

• http://dx.doi.org/10.1016/j.spl.2016.07.001