Please use this identifier to cite or link to this item: http://ktisis.cut.ac.cy/handle/10488/6666
Title: Artificially augmented samples, shrinkage, and mean squared error reduction
Authors: Yatracos, Yannis G. 
Keywords: Multiple imputation (Statistics)
U-statistics
Issue Date: 2005
Publisher: Taylor & Francis Online
Source: Journal of the American Statistical Association, 2005, Volume 100, Issue 472, Pages 1168-1175
Abstract: An inequality is provided that determines when shrinkage reduces the mean squared error (MSE) of an unbiased estimate. Artificially augmented samples are then used to obtain, among others, shrinkage estimates of the population's variance and covariance, which improve the unbiased estimates for all parameter values and for all probability models with marginals having finite second moments, and alternative jackknife estimates that complement the usual jackknife estimates in reducing the MSE.
URI: http://ktisis.cut.ac.cy/handle/10488/6666
ISSN: 01621459
DOI: 10.1198/016214505000000321
Rights: © 2005 American Statistical Association.
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