Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/2147
Title: Artificially augmented samples, shrinkage, and mean squared error reduction
Authors: Yatracos, Yannis G. 
metadata.dc.contributor.other: Γιατράκος, Γιάννης
Major Field of Science: Social Sciences
Keywords: Multiple imputation (Statistics);U-statistics
Issue Date: 2005
Source: Journal of the American Statistical Association, 2005, vol. 100, no. 472, pp. 1168-1175
Volume: 100
Issue: 472
Start page: 1168
End page: 1175
Journal: Journal of the American Statistical Association 
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: https://hdl.handle.net/20.500.14279/2147
ISSN: 01621459
DOI: 10.1198/016214505000000321
Rights: © American Statistical Association
Attribution-NonCommercial-NoDerivs 3.0 United States
Type: Article
Affiliation: National University of Singapore 
Affiliation : National University of Singapore 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

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