Model selection in binary and tobit quantile regression using the Gibbs sampler

Authors:
Yonggang Ji;Nan Lin;Baoxue Zhang
Affiliations:
Key Laboratory for Applied Statistics of MOE and School of Mathematics and Statistics, Northeast Normal University, Changchun, China;Department of Mathematics, Washington University in St. Louis, One Brookings Drive, Saint Louis, MO 63130, USA;Key Laboratory for Applied Statistics of MOE and School of Mathematics and Statistics, Northeast Normal University, Changchun, China
Venue:
Computational Statistics & Data Analysis
Year:
2012

Citing 2
Cited 2

Regularized simultaneous model selection in multiple quantiles regression

Computational Statistics & Data Analysis
Likelihood-free Bayesian estimation of multivariate quantile distributions

Computational Statistics & Data Analysis

Editorial for the special issue on quantile regression and semiparametric methods

Computational Statistics & Data Analysis
Conjugate priors and variable selection for Bayesian quantile regression

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.03

Visualization

Abstract

A stochastic search variable selection approach is proposed for Bayesian model selection in binary and tobit quantile regression. A simple and efficient Gibbs sampling algorithm was developed for posterior inference using a location-scale mixture representation of the asymmetric Laplace distribution. The proposed approach is then illustrated via five simulated examples and two real data sets. Results show that the proposed method performs very well under a variety of scenarios, such as the presence of a moderately large number of covariates, collinearity and heterogeneity.