Journal article
Estimation Optimality of Corrected AIC and Modified Cp in Linear Regression
International statistical review, Vol.74(2), pp.161-168
Received June 2005, accepted November 2005
08/2006
DOI: 10.1111/j.1751-5823.2006.tb00167.x
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.
Details
- Title: Subtitle
- Estimation Optimality of Corrected AIC and Modified Cp in Linear Regression
- Creators
- Simon L Davies - Pfizer Global Pharmaceuticals, Inc., USAAndrew A Neath - University of Iowa, BiostatisticsJoseph E Cavanaugh - University of Iowa, Biostatistics
- Resource Type
- Journal article
- Publication Details
- International statistical review, Vol.74(2), pp.161-168
- Edition
- Received June 2005, accepted November 2005
- DOI
- 10.1111/j.1751-5823.2006.tb00167.x
- ISSN
- 0306-7734
- eISSN
- 1751-5823
- Publisher
- Blackwell Publishing Ltd
- Number of pages
- 8
- Language
- English
- Date published
- 08/2006
- Academic Unit
- Statistics and Actuarial Science; Biostatistics; Injury Prevention Research Center
- Record Identifier
- 9984214826502771
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