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August 2009 Model Assessment Tools for a Model False World
Bruce Lindsay, Jiawei Liu
Statist. Sci. 24(3): 303-318 (August 2009). DOI: 10.1214/09-STS302


A standard goal of model evaluation and selection is to find a model that approximates the truth well while at the same time is as parsimonious as possible. In this paper we emphasize the point of view that the models under consideration are almost always 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 a model credibility index, which is designed to serve as a one-number summary measure of model adequacy. We define the index to be the maximum sample size at which samples from the model and those from the true data generating mechanism are nearly indistinguishable. We use standard notions from hypothesis testing to make this definition precise. We use data subsampling to estimate the index. We show that the definition leads us to some new ways of viewing models as flawed but useful. The concept is an extension of the work of Davies [Statist. Neerlandica 49 (1995) 185–245].


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Bruce Lindsay. Jiawei Liu. "Model Assessment Tools for a Model False World." Statist. Sci. 24 (3) 303 - 318, August 2009.


Published: August 2009
First available in Project Euclid: 31 March 2010

zbMATH: 1329.62099
MathSciNet: MR2757432
Digital Object Identifier: 10.1214/09-STS302

Keywords: bootstrap , model credibility index , Model selection , normality , statistical distance

Rights: Copyright © 2009 Institute of Mathematical Statistics


Vol.24 • No. 3 • August 2009
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