Reversing the Stein Effect
Copyright policy )Reversing the Stein Effect
The Reverse Stein Effect is identified and illustrated: A statistician who shrinks his/her data toward a point chosen without reliable knowledge about the underlying value of the parameter to be estimated but based instead upon the observed data will not be protected by the minimax property of shrinkage estimators such as that of James and Stein, but instead will likely incur a greater error than if shrinkage were not used.
Published in at http://dx.doi.org/10.1214/09-STS278 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org)
- University of Washington United States
- Washington State University United States
- UNIVERSITY OF WASHINGTON United States
- University of Mary United States
FOS: Computer and information sciences, Ridge regression; shrinkage estimators (Lasso), James-Stein estimator, Bayesian inference, Point estimation, multivariate normal distribution, Bayes and empirical Bayes estimators, James–Stein estimator, shrinkage estimator, Methodology (stat.ME), Empirical decision procedures; empirical Bayes procedures, Statistics - Methodology
FOS: Computer and information sciences, Ridge regression; shrinkage estimators (Lasso), James-Stein estimator, Bayesian inference, Point estimation, multivariate normal distribution, Bayes and empirical Bayes estimators, James–Stein estimator, shrinkage estimator, Methodology (stat.ME), Empirical decision procedures; empirical Bayes procedures, Statistics - Methodology
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