View : 27 Download: 0

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
소병수오만숙
SCOPUS Author ID
소병수scopus; 오만숙scopus
Issue Date
2016
Journal Title
Statistics and Probability Letters
ISSN
0167-7152JCR Link
Citation
vol. 118, pp. 177 - 181
Keywords
Bayes factorBayesian model selectionMarkov chain Monte CarloQuantile regression
Publisher
Elsevier B.V.
Indexed
SCIE; SCOPUS WOS scopus
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
Appears in Collections:
자연과학대학 > 통계학전공 > Journal papers
Files in This Item:
There are no files associated with this item.
Export
RIS (EndNote)
XLS (Excel)
XML


qrcode

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.

BROWSE