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https://hdl.handle.net/20.500.14279/2147| Τίτλος: | Artificially augmented samples, shrinkage, and mean squared error reduction | Συγγραφείς: | Yatracos, Yannis G. | metadata.dc.contributor.other: | Γιατράκος, Γιάννης | Major Field of Science: | Social Sciences | Λέξεις-κλειδιά: | Multiple imputation (Statistics);U-statistics | Ημερομηνία Έκδοσης: | 2005 | Πηγή: | Journal of the American Statistical Association, 2005, vol. 100, no. 472, pp. 1168-1175 | Volume: | 100 | Issue: | 472 | Start page: | 1168 | End page: | 1175 | Περιοδικό: | Journal of the American Statistical Association | Περίληψη: | 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 |
| Εμφανίζεται στις συλλογές: | Άρθρα/Articles |
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