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Unpublished Paper
BAYESIAN FUNCTIONAL DATA ANALYSIS USING WinBUGS
Johns Hopkins University, Dept. of Biostatistics Working Papers
  • Ciprian M. Crainiceanu, Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
  • A. Jeffrey Goldsmith, Johns Hopkins Bloomberg School of Public Health, Department of Biostatistics
Date of this Version
11-3-2009
Comments
Accepted by Journal of Statistical Software
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)the mixed model representation of functional models, which provides a modular approach to model extension; and 3) the 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.
Disciplines
Citation Information
Ciprian M. Crainiceanu and A. Jeffrey Goldsmith. "BAYESIAN FUNCTIONAL DATA ANALYSIS USING WinBUGS" (2009)
Available at: http://works.bepress.com/ciprian_crainiceanu/7/