Griffin, Jim E. and Steel, Mark F. J.. (2010) Bayesian nonparametric modelling with the Dirichlet process regression smoother. Statistica Sinica, Vol.20 (No.4). pp. 1507-1527. ISSN 1017-0405

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Abstract

In this paper we discuss implementing Bayesian fully nonparametric regression by defining a process prior on distributions that depend on covariates. We consider the problem of centring our process over a class of regression models, and propose fully nonparametric regression models with flexible location structures. We also introduce an extension of a dependent finite mixture model proposed by Chung and Dunson (2011) to a dependent infinite mixture model and propose a specific prior, the Dirichlet Process Regression Smoother, which allows us to control the smoothness of the process. Computational methods are developed for the models described. Results are presented for simulated and for real data examples.

Item Type:	Journal Article
Divisions:	Faculty of Science > Statistics
Journal or Publication Title:	Statistica Sinica
Publisher:	Academia Sinica * Institute of Statistical Science
ISSN:	1017-0405
Date:	October 2010
Volume:	Vol.20
Number:	No.4
Page Range:	pp. 1507-1527
Status:	Peer Reviewed
Publication Status:	Published
Access rights to Published version:	Restricted or Subscription Access
URI:	http://wrap.warwick.ac.uk/id/eprint/43045

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