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Bayesian Analysis for Penalized Spline Regression Using Win BUGS

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Author Info
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)
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.

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File URL: http://www.bepress.com/cgi/viewcontent.cgi?article=1040&context=jhubiostat
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Publisher Info
Paper provided by Berkeley Electronic Press in its series Johns Hopkins University Dept. of Biostatistics Working Paper Series with number 1040.

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Date of creation: 08 Sep 2004
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Handle: RePEc:bep:jhubio:1040

Note: oai:bepress.com:jhubiostat-1040
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Related research
Keywords: MCMC; Semiparametric regression; Software;

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  1. Berry S. M. & Carroll R. J & Ruppert D., 2002. "Bayesian Smoothing and Regression Splines for Measurement Error Problems," Journal of the American Statistical Association, American Statistical Association, vol. 97, pages 160-169, March. [Downloadable!] (restricted)
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This page was last updated on 2009-12-15.


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