JSM 2004 - Toronto

Abstract #300884

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Activity Number: 89
Type: Contributed
Date/Time: Monday, August 9, 2004 : 8:30 AM to 10:20 AM
Sponsor: Section on Bayesian Statistical Science
Abstract - #300884
Title: Bayesian Inference in Generalized Additive Mixed Models with Normal Random Effects
Author(s): Yisheng Li*+ and Xihong Lin
Companies: University of Texas M.D. Anderson Cancer Center and University of Michigan
Address: 1515 Holcombe Blvd., Unit 447, Houston, TX, 77030,
Keywords: Bayesian generalized additive mixed model ; integrated Wiener prior ; smoothing spline ; generalized linear mixed model ; Gibbs sampling ; adaptive rejection sampling
Abstract:

We propose Bayesian generalized additive mixed models for correlated data, which arise frequently in studies involving clustered, hierarchical, and spatial designs. The models allow for additive functional dependence of an outcome variable on covariates by using nonparametric regression and account for correlation between observations using random effects. Partially improper integrated Wiener priors are used for the nonparametric functions and the resulting estimators are cubic smoothing splines. Systematic inference on model parameters can be made within a modified generalized linear mixed model framework. Computation is carried out using Gibbs sampling. We illustrate the proposed approach by analyzing an infectious disease dataset and compare its performance with that of the double penalized quasi-likelihood approach through simulation.

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Revised March 2004