Abstract #302083

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JSM 2003 Abstract #302083
Activity Number: 293
Type: Contributed
Date/Time: Tuesday, August 5, 2003 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #302083
Title: Bayesian Analysis of Log-Linear Models
Author(s): Jenting Wang*+ and John S. J. Hsu
Companies: State University of New York College at Oneonta and University of California, Santa Barbara
Address: Mathematical Sciences, Oneonta, NY, 13820,
Keywords: log-linear models ; hierarchical Bayesian ; Bayesian smoothing
Abstract:

We consider smooth Bayesian estimates (Hsu and Leonard 1997) for the log-linear regression functions. A model containing a global shrinkage parameter and a local smoothness parameter is used for analysis. The global shrinkage and local smoothness parameters evaluated using the hierarchical Bayesian techniques are such that their estimates, while based on standard models (like the log-linear models), are still able to produce a fit that would be more closely representative of the observed data. Further, the procedures enable us to make predictions at points in the design space beyond the range of the data set. Exact residual analysis may also be obtained and used for checking the validity of the hypothesized models. A dataset concerning the relationship between years of smoking and lung cancer death rates is used for demonstration.

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