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AIC and Large Samples

Kieseppä, I. A. (2002) AIC and Large Samples. [Preprint]

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Abstract

I discuss the behavior of the Akaike Information Criterion in the limit when the sample size grows. I show the falsity of the claim made recently by Stanley Mulaik in Philosophy of Science that AIC would not distinguish between saturated and other correct factor analytic models in this limit. I explain the meaning and demonstrate the validity of the familiar, more moderate criticism that AIC is not a consistent estimator of the number of parameters of the smallest correct model. I also give a short explanation why this feature of AIC is compatible with the motives of using it.


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Item Type: Preprint
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Kieseppä, I. A.
Keywords: Probability Statistics, Confirmation/Induction, Model Selection Criteria
Depositing User: Program Committee
Date Deposited: 23 Mar 2003
Last Modified: 07 Oct 2010 15:11
Item ID: 1079
URI: http://philsci-archive.pitt.edu/id/eprint/1079

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