Article contents
AIC and Large Samples
Published online by Cambridge University Press: 01 January 2022
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 for using it.
- Type
- Confirmation and Statistical Inference
- Information
- Philosophy of Science , Volume 70 , Issue 5: Proceedings of the 2002 Biennial Meeting of The Philosophy of Science Association. Part I: Contributed Papers , December 2003 , pp. 1265 - 1276
- Copyright
- Copyright © The Philosophy of Science Association
Footnotes
I would like to express my gratitude to Stanley Mulaik and Malcolm Forster for our discussions on the topics addressed in this paper.
References
- 3
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