Published by the Foundation for Open Access Statistics
Editors-in-chief: Bettina Grün, Torsten Hothorn, Edzer Pebesma, Achim Zeileis    ISSN 1548-7660; CODEN JSSOBK
bayesQR: A Bayesian Approach to Quantile Regression | Benoit | Journal of Statistical Software
Authors: Dries F. Benoit, Dirk Van den Poel
Title: bayesQR: A Bayesian Approach to Quantile Regression
Abstract: After its introduction by Koenker and Basset (1978), quantile regression has become an important and popular tool to investigate the conditional response distribution in regression. The R package bayesQR contains a number of routines to estimate quantile regression parameters using a Bayesian approach based on the asymmetric Laplace distribution. The package contains functions for the typical quantile regression with continuous dependent variable, but also supports quantile regression for binary dependent variables. For both types of dependent variables, an approach to variable selection using the adaptive lasso approach is provided. For the binary quantile regression model, the package also contains a routine that calculates the fitted probabilities for each vector of predictors. In addition, functions for summarizing the results, creating traceplots, posterior histograms and drawing quantile plots are included. This paper starts with a brief overview of the theoretical background of the models used in the bayesQR package. The main part of this paper discusses the computational problems that arise in the implementation of the procedure and illustrates the usefulness of the package through selected examples.

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Paper: bayesQR: A Bayesian Approach to Quantile Regression     Download PDF (Downloads: 356)
bayesQR_2.3.tar.gz: R source package Download (Downloads: 11; 33KB)
v76i07.R: R replication code Download (Downloads: 19; 6KB)

DOI: 10.18637/jss.v076.i07

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Paper: Creative Commons Attribution 3.0 Unported License
Code: GNU General Public License (at least one of version 2 or version 3) or a GPL-compatible license.