Bayesian Analysis for Penalized Spline Regression Using Win BUGS

Ciprian Crainiceanu, David Ruppert and M.P. Wand
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Ciprian Crainiceanu: Johns Hokins Bloomberg School of Public Health, Department of Biostatistics
David Ruppert: Cornell University, School of Operational Research & Industrial Engineering
M.P. Wand: Department of Statistics, School of Mathematics, University of South Wales

No 1040, Johns Hopkins University Dept. of Biostatistics Working Paper Series from Berkeley Electronic Press

Abstract: Penalized splines can be viewed as BLUPs in a mixed model framework, which allows the use of mixed model software for smoothing. Thus, software originally developed for Bayesian analysis of mixed models can be used for penalized spline regression. Bayesian inference for nonparametric models enjoys the flexibility of nonparametric models and the exact inference provided by the Bayesian inferential machinery. This paper provides a simple, yet comprehensive, set of programs for the implementation of nonparametric Bayesian analysis in WinBUGS.

Keywords: MCMC; Semiparametric regression; Software (search for similar items in EconPapers)
Date: 2004-09-08
Note: oai:bepress.com:jhubiostat-1040
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