Translator Disclaimer
August 2006 Estimation Optimality of Corrected AIC and Modified Cp in Linear Regression
Simon L. Davies, Andrew A. Neath, Joseph E. Cavanaugh
Internat. Statist. Rev. 74(2): 161-168 (August 2006).

Abstract

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.

Citation

Download Citation

Simon L. Davies. Andrew A. Neath. Joseph E. Cavanaugh. "Estimation Optimality of Corrected AIC and Modified Cp in Linear Regression." Internat. Statist. Rev. 74 (2) 161 - 168, August 2006.

Information

Published: August 2006
First available in Project Euclid: 24 July 2006

Keywords: AICc , Gauss discrepancy , Kullback-Leibler discrepancy , MC_p , model selection criteria

Rights: Copyright © 2006 International Statistical Institute

JOURNAL ARTICLE
8 PAGES

This article is only available to subscribers.
It is not available for individual sale.
+ SAVE TO MY LIBRARY

SHARE
Vol.74 • No. 2 • August 2006
Back to Top