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Proceedings of the American Mathematical Society
Proceedings of the American Mathematical Society
ISSN 1088-6826(online) ISSN 0002-9939(print)

 

Bias and variance reduction in estimation of model dimension


Authors: Wei-Yin Loh and Xiaodong Zheng
Journal: Proc. Amer. Math. Soc. 122 (1994), 1263-1272
MSC: Primary 62J05; Secondary 62F11
MathSciNet review: 1211583
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Abstract: The problem of estimating the number of regressors to include in a linear regression model is considered. Estimators based on the final prediction error and Akaike's criterion frequently have large positive bias. Shrinkage correction factors and bootstrapping are used to produce new estimators with reduced bias. The asymptotic bias and mean-squared errors of these estimators are derived analytically. Finite-sample estimates are obtained by simulation.


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Additional Information

DOI: http://dx.doi.org/10.1090/S0002-9939-1994-1211583-3
PII: S 0002-9939(1994)1211583-3
Keywords: Akaike criterion, cross-validation, final prediction error, model selection
Article copyright: © Copyright 1994 American Mathematical Society