Please use this identifier to cite or link to this item: https://doi.org/10.1198/016214505000000321
Title: Artificially augmented samples, shrinkage, and mean squared error reduction
Authors: Yatracos, Y.G. 
Keywords: Augmented samples
Bias
Jackknife
Mean squared error
Multiple imputation
Shrinkage
U-statistics
Variance estimation
Issue Date: Dec-2005
Citation: Yatracos, Y.G. (2005-12). Artificially augmented samples, shrinkage, and mean squared error reduction. Journal of the American Statistical Association 100 (472) : 1168-1175. ScholarBank@NUS Repository. https://doi.org/10.1198/016214505000000321
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. © 2005 American Statistical Association.
Source Title: Journal of the American Statistical Association
URI: http://scholarbank.nus.edu.sg/handle/10635/105009
ISSN: 01621459
DOI: 10.1198/016214505000000321
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