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Bayesian Analysis for Penalized Spline Regression Using Win BUGS

Author

Listed:
  • 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.

Suggested Citation

  • Ciprian Crainiceanu & David Ruppert & M.P. Wand, 2004. "Bayesian Analysis for Penalized Spline Regression Using Win BUGS," Johns Hopkins University Dept. of Biostatistics Working Paper Series 1040, Berkeley Electronic Press.
    • Handle: RePEc:bep:jhubio:1040
    Note: oai:bepress.com:jhubiostat-1040
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    References listed on IDEAS

    as
    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.
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