Bayesian quantile regression for partially linear additive models

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review

24 Scopus Citations
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Author(s)

Detail(s)

Original languageEnglish
Pages (from-to)651-668
Journal / PublicationStatistics and Computing
Volume25
Issue number3
Online published11 Mar 2014
Publication statusPublished - May 2015
Externally publishedYes

Abstract

In this article, we develop a semiparametric Bayesian estimation and model selection approach for partially linear additive models in conditional quantile regression. The asymmetric Laplace distribution provides a mechanism for Bayesian inferences of quantile regression models based on the check loss. The advantage of this new method is that nonlinear, linear and zero function components can be separated automatically and simultaneously during model fitting without the need of pre-specification or parameter tuning. This is achieved by spike-and-slab priors using two sets of indicator variables. For posterior inferences, we design an effective partially collapsed Gibbs sampler. Simulation studies are used to illustrate our algorithm. The proposed approach is further illustrated by applications to two real data sets.

Research Area(s)

  • Additive models, Markov chain Monte Carlo, Quantile regression, Variable selection

Citation Format(s)

Bayesian quantile regression for partially linear additive models. / Hu, Yuao; Zhao, Kaifeng; Lian, Heng.
In: Statistics and Computing, Vol. 25, No. 3, 05.2015, p. 651-668.

Research output: Journal Publications and ReviewsRGC 21 - Publication in refereed journalpeer-review