JSM 2005 Online Program

Abstract #304448

This is the preliminary program for the 2005 Joint Statistical Meetings in Minneapolis, Minnesota. Currently included in this program is the "technical" program, schedule of invited, topic contributed, regular contributed and poster sessions; Continuing Education courses (August 7-10, 2005); and Committee and Business Meetings. This on-line program will be updated frequently to reflect the most current revisions.

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Activity Number:	135
Type:	Contributed
Date/Time:	Monday, August 8, 2005 : 10:30 AM to 12:20 PM
Sponsor:	IMS
Abstract - #304448
Title:	Model Selection Using the Discrimination Sample Size
Author(s):	Jiawei Liu*+ and Bruce G. Lindsay
Companies:	Georgia State University and The Pennsylvania State University
Address:	718 COE, Atlanta, GA, 30303, United States
Keywords:	model selection ; sample size ; AIC ; BIC
Abstract:	In the statistical process of model evaluation and selection, the usual goal is to find a model that approximates the truth well while being as parsimonious as possible. In this paper, we emphasize the point of view that the models under consideration are generally false, if viewed realistically, and so we should analyze model adequacy from that point of view. We investigate this issue in large samples by looking at two types of sample-size indices, which are designed to serve as one-number summary measures of model adequacy. We define these indices to be the maximum sample size that the model will sustain without creating a clear distinction between samples from the model and those from the true data generating mechanism. We show that these definitions lead us to a new way of viewing models as flawed but useful. These concepts are an extension of important work of Davies (1995).

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