Bayesian analysis for penalized spline regression using WinBUGS

被引:0
作者
Crainiceanu, CM
Ruppert, D
Wand, MP
机构
[1] Johns Hopkins Univ, Dept Biostat, Baltimore, MD 21205 USA
[2] Cornell Univ, Sch Operat Res & Ind Engn, Ithaca, NY 14853 USA
[3] Univ New S Wales, Sch Math, Dept Stat, Sydney, NSW 2052, Australia
关键词
MCMC; semiparametric regression;
D O I
暂无
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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. Good mixing properties of the MCMC chains are obtained by using low-rank thin-plate splines, while simulation times per iteration are reduced employing WinBUGS specific computational tricks.
引用
收藏
页数:24
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