Abstract #301992

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JSM 2003 Abstract #301992
Activity Number: 366
Type: Contributed
Date/Time: Wednesday, August 6, 2003 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #301992
Title: Bayesian Model Diagnostics Based on Time Series
Author(s): Mario Peruggia*+
Companies: The Ohio State University
Address: Dept. of Statistics 404 Cockins Hall, Columbus, OH, 43210-1247,
Keywords: hierarchical models ; residuals ; dependence ; analysis of response times ; error distribution
Abstract:

Hierarchical Bayes models provide a natural way of incorporating covariate information into the inferential process through the elaboration of regression equations for one or more of the model parameters, with errors that are often assumed to be iid Gaussian. Unfortunately, building adequate regression models is a complicated art form that requires the practitioner to make numerous decisions along the way. Assessing the validity of the modeling decisions is often difficult. To help with this assessment a time series structure in the probability model for the errors is specified that incorporates the iid model as a special case. Severe departures from independence can be detected by examining the posterior distribution of the parameters of the time series. Strong dependencies provide evidence that some other aspects (typically conditional means) of the model have been misspecified. The methodology is illustrated through several examples including an analysis of response times for word recognition.

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