Abstract #302038

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JSM 2003 Abstract #302038
Activity Number: 173
Type: Contributed
Date/Time: Monday, August 4, 2003 : 2:00 PM to 3:50 PM
Sponsor: Section on Bayesian Stat. Sciences
Abstract - #302038
Title: Model Selection Consistency and Error Rates in Log Spline Models
Author(s): Yongsung Joo*+ and George Casella and Martin T. Wells
Companies: University of Florida and University of Florida and Cornell University
Address: 1105 Fort Clarke Blvd., Gainesville, FL, 32606-7140,
Keywords: model selection ; log spline ; consistency
Abstract:

Since the introduction of Akaike's information criteria (AIC) in 1973, many different information criteria have been developed and widely used in model selection. Many papers concerning the justification of criteria followed, especially with respect to consistency and model selection error rate. If a particular criterion chooses the correct model as the sample size increases to infinity, the criterion is consistent, and inconsistent otherwise. We explore the conditions for consistent information criteria in log spline models, when the number of parameters increases with the sample size, and show that the model selection error rate alone is not sufficient to judge the performance of model selection criterion.

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