JSM 2004 - Toronto

Abstract #301564

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Activity Number: 74
Type: Topic Contributed
Date/Time: Monday, August 9, 2004 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #301564
Title: Bayesian Hierarchical Linear Mixed Models for Additive Smoothing
Author(s): Dongchu Sun*+ and Paul L. Speckman
Companies: University of Missouri, Columbia and University of Missouri, Columbia
Address: 146 Middlebush Hall, Columbia, MO, 65201,
Keywords: smoothing spline ; additive models ; hierarchical models ; variance components ; noninformative priors
Abstract:

Bayesian hierarchical models have been used for smoothing splines, thin-plate splines, and L-splines. In analyzing high-dimensional datasets, additive models and backfitting methods are often used. A full Bayesian analysis for such models may include a large number of random effects, many of which are not intuitive, so researchers typically use noninformative improper or nearly improper priors. We investigate propriety of the posterior for these cases. Our findings extend known results for normal linear mixed models to certain cases with Bayesian additive smoothing spline models.

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