Artificially augmented samples, shrinkage, and mean squared error reduction
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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.
Keywords
Augmented samples, Bias, Jackknife, Mean squared error, Multiple imputation, Shrinkage, U-statistics, Variance estimation
Source Title
Journal of the American Statistical Association
Publisher
Series/Report No.
Collections
Rights
Date
2005-12
DOI
10.1198/016214505000000321
Type
Article