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Title: Artificially augmented samples, shrinkage, and mean squared error reduction
Authors: Yatracos, Y.G. 
Keywords: Augmented samples
Mean squared error
Multiple imputation
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.
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
ISSN: 01621459
DOI: 10.1198/016214505000000321
Appears in Collections:Staff Publications

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