Bayesian functional data analysis using WinBUGS

Ciprian M. Crainiceanu, A. Jeffrey Goldsmith

Research output: Contribution to journalArticlepeer-review

Abstract

We provide user friendly software for Bayesian analysis of functional data models using WinBUGS 1.4. The excellent properties of Bayesian analysis in this context are due to: (1) dimensionality reduction, which leads to low dimensional projection bases; (2) mixed model representation of functional models, which provides a modular approach to model extension; and (3) orthogonality of the principal component bases, which contributes to excellent chain convergence and mixing properties. Our paper provides one more, essential, reason for using Bayesian analysis for functional models: the existence of software.

Original languageEnglish (US)
Pages (from-to)1-33
Number of pages33
JournalJournal of Statistical Software
Volume32
Issue number11
DOIs
StatePublished - Jan 2010

Keywords

  • Covariance
  • MCMC
  • Mixed effects
  • Smoothing

ASJC Scopus subject areas

  • Software
  • Statistics and Probability
  • Statistics, Probability and Uncertainty

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