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Model Selection for Extended Quasi-Likelihood Models in Small Samples

Clifford M. Hurvich and Chih-Ling Tsai
Biometrics
Vol. 51, No. 3 (Sep., 1995), pp. 1077-1084
DOI: 10.2307/2533006
Stable URL: http://www.jstor.org/stable/2533006
Page Count: 8
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Since scans are not currently available to screen readers, please contact JSTOR User Support for access. We'll provide a PDF copy for your screen reader.
Model Selection for Extended Quasi-Likelihood Models in Small Samples
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Abstract

We develop a small sample criterion (AICc) for the selection of extended quasi-likelihood models. In contrast to the Akaike information criterion (AIC), AICc provides a more nearly unbiased estimator for the expected Kullback-Leibler information. Consequently, it often selects better models than AIC in small samples. For the logistic regression model, Monte Carlo results show that AICc outperforms AIC, Pregibon's (1979, Data Analytic Methods for Generalized Linear Models. Ph.D. thesis. University of Toronto) C*p, and the Cp selection criteria of Hosmer et al. (1989, Biometrics 45, 1265-1270). Two examples are presented.

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