Abstract #301932

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JSM 2002 Abstract #301932
Activity Number: 254
Type: Contributed
Date/Time: Tuesday, August 13, 2002 : 2:00 PM to 3:50 PM
Sponsor: General Methodology
Abstract - #301932
Title: Box and Cox Transformation Model Selections
Author(s): Chih-Ling Tsai*+
Affiliation(s): University of California, Davis
Address: , Davis, CA, 95616-8609, USA
Keywords: AIC ; AICC ; BIC ; MIC ; Marginal likelihood ; Model Selection
Abstract:

We develop a two-stage model selection procedure for choosing regression covariates for Box-Cox transformation models. For the two criteria that result from this procedure, we show that the Akaike information criterion, AICC, is efficient, and that the marginal information criterion, MIC, is consistent. We illustrate the use of this procedure via simulation studies and analysis of a real example. Monte Carlo studies comparing AICC, MIC, AIC, BIC, FIC, and FPE show that MIC performs well except when the sample size is small and the signal-to-noise ratio is weak. In this case, AICC is recommended.

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