JSM 2004 - Toronto

Abstract #301036

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Activity Number: 230
Type: Contributed
Date/Time: Tuesday, August 10, 2004 : 10:30 AM to 12:20 PM
Sponsor: IMS
Abstract - #301036
Title: An Optimality Result for a Cross-validation Selection Criterion
Author(s): Andrew Neath*+ and Joseph Cavanaugh and Simon Davies
Companies: Southern Illinois University, Edwardsville and University of Iowa and Centocor, Inc.
Address: , , ,
Keywords: Kullback divergence ; Akaike information
Abstract:

Consider a model selection problem where one looks to decide which input variables are needed in developing the best prediction function. Cross-validation procedures have been seen to work well in practice here. We show how cross validation can be presented using a discrepancy function approach. As an example, the predictive divergence criterion (PDC) is introduced. The determination of the minimum variance unbiased estimate of the target discrepancy leads to an optimal selection criterion under conditions set forth by this cross-validation procedure.

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