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Unpublished Paper
Bayesian Analysis for Penalized Spline Regression Using Win BUGS
Johns Hopkins University, Dept. of Biostatistics Working Papers
  • Ciprian M. 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
Date of this Version
12-1-2007
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. MCMC mixing is substantially improved from the previous versions by using low{rank thin{plate splines instead of truncated polynomial basis. Simulation time per iteration is reduced 5 to 10 times using a computational trick.
Citation Information
Ciprian M. Crainiceanu, David Ruppert and M.P. Wand. "Bayesian Analysis for Penalized Spline Regression Using Win BUGS" (2007)
Available at: http://works.bepress.com/ciprian_crainiceanu/8/