Thompson, Paul A., Cai, Yuzhi, Moyeed, Rana, Reeve, Dominic and Stander, Julian (2010) Bayesian nonparametric quantile regression using splines. Computational Statistics & Data Analysis, 54 (4). pp. 1138-1150. doi:10.1016/j.csda.2009.09.004

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Abstract

A new technique based on Bayesian quantile regression that models the dependence of a quantile of one variable on the values of another using a natural cubic spline is presented. Inference is based on the posterior density of the spline and an associated smoothing parameter and is performed by means of a Markov chain Monte Carlo algorithm. Examples of the application of the new technique to two real environmental data sets and to simulated data for which polynomial modelling is inappropriate are given. An aid for making a good choice of proposal density in the Metropolis–Hastings algorithm is discussed. The new nonparametric methodology provides more flexible modelling than the currently used Bayesian parametric quantile regression approach.

Item Type:	Journal Article
Divisions:	Faculty of Social Sciences > Centre for Educational Development, Appraisal and Research (CEDAR)
Journal or Publication Title:	Computational Statistics & Data Analysis
Publisher:	Elsevier Science Ltd
ISSN:	0167-9473
Official Date:	1 April 2010
Dates:	Date Event 1 April 2010 Published 15 September 2009 Available 7 September 2009 Accepted
Volume:	54
Number:	4
Page Range:	pp. 1138-1150
DOI:	10.1016/j.csda.2009.09.004
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access

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