Statistical Science

Reversing the Stein Effect

Michael D. Perlman and Sanjay Chaudhuri

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Abstract

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.

Article information

Source
Statist. Sci., Volume 27, Number 1 (2012), 135-143.

Dates
First available in Project Euclid: 14 March 2012

Permanent link to this document
https://projecteuclid.org/euclid.ss/1331729987

Digital Object Identifier
doi:10.1214/09-STS278

Mathematical Reviews number (MathSciNet)
MR2953500

Zentralblatt MATH identifier
1330.62293

Keywords
James–Stein estimator shrinkage estimator Bayes and empirical Bayes estimators multivariate normal distribution

Citation

Perlman, Michael D.; Chaudhuri, Sanjay. Reversing the Stein Effect. Statist. Sci. 27 (2012), no. 1, 135--143. doi:10.1214/09-STS278. https://projecteuclid.org/euclid.ss/1331729987


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