International Statistical Review

Estimation Optimality of Corrected AIC and Modified Cp in Linear Regression

Simon L. Davies, Andrew A. Neath, and Joseph E. Cavanaugh

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Model selection criteria often arise by constructing unbiased or approximately unbiased estimators of measures known as expected overall discrepancies (Linhart & Zucchini, 1986, p. 19). Such measures quantify the disparity between the true model (i.e., the model which generated the observed data) and a fitted candidate model. For linear regression with normally distributed error terms, the "corrected" Akaike information criterion and the "modified" conceptual predictive statistic have been proposed as exactly unbiased estimators of their respective target discrepancies. We expand on previous work to additionally show that these criteria achieve minimum variance within the class of unbiased estimators.

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Internat. Statist. Rev., Volume 74, Number 2 (2006), 161-168.

First available in Project Euclid: 24 July 2006

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AICc Gauss discrepancy Kullback-Leibler discrepancy MC_p Model selection criteria


Davies, Simon L.; Neath, Andrew A.; Cavanaugh, Joseph E. Estimation Optimality of Corrected AIC and Modified Cp in Linear Regression. Internat. Statist. Rev. 74 (2006), no. 2, 161--168.

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