JSM 2005 Online Program

Abstract #304692

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	492
Type:	Contributed
Date/Time:	Thursday, August 11, 2005 : 8:30 AM to 10:20 AM
Sponsor:	Section on Statistics and the Environment
Abstract - #304692
Title:	Bayesian Model Averaging in Semiparametric Models of Air Quality and Respiratory Health
Author(s):	Chava Zibman*+
Companies:	The University of Chicago
Address:	Dept of Statistics, Chicago, IL, 60637, United States
Keywords:	Air pollution ; Bayesian Model Averaging ; Poisson regression
Abstract:	Time-series analyses of the relationship between pollution and morbidity generally rely on Poisson regression models with smooth functions of time to account for unobserved time-varying confounders. This paper presents the analyses of daily time series asthma-prescription data throughout four summers in Chicago. Rather than choosing a single amount of smoothness in the adjustment for the confounding variable, we use Bayesian model averaging to adjust for uncertainty due to the choice of smoothing parameter. We conduct a simulation study to compare semiparametric fits using Bayesian model averaging to those obtained by several model selection methods for choosing the degree of smoothing. In addition, in these simulations, we pay special attention to the choice of priors for the confounding function and the effects these priors have on the comparison results.

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