Kieseppä, I. A.
AIC and Large Samples.
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.
|Kieseppä, I. A.|
||Probability Statistics, Confirmation/Induction, Model Selection Criteria
||23 Mar 2003
||07 Oct 2010 15:11
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