NDLI: Bias and Variance Reduction in Estimation of Model Dimension

Content Provider	Scilit
Author	Loh, Wei-Yin Zheng, Xiaodong
Copyright Year	1994
Description	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.
Related Links	http://www.ams.org/proc/1994-122-04/S0002-9939-1994-1211583-3/S0002-9939-1994-1211583-3.pdf
Ending Page	1272
Page Count	10
Starting Page	1263
ISSN	00029939
e-ISSN	10886826
DOI	10.2307/2161197
Journal	Proceedings of the American Mathematical Society
Issue Number	4
Volume Number	122
Language	English
Publisher	Duke University Press
Access Restriction	Open
Subject Keyword	Mathematical Social Sciences Variance Reduction Model Dimension Bias and Variance Reduction in Estimation
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics

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Bias and Variance Reduction in Estimation of Model Dimension