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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