INTEGRAL INEQUALITY FOR MINIMAXITY IN THE STEIN PROBLEM

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概要

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• In the estimation of a multivariate normal mean, it is shown that the problem of deriving shrinkage estimators improving on the maximum likelihood estimator can be reduced to that of solving an integral inequality. The integral inequality not only provides a more general condition than a conventional differential inequality studied in the literature, but also handles non-differentiable or discontinuous estimators. The paper also gives general conditions on prior distributions such that the resulting generalized Bayes estimators are minimax. Finally, a simple proof for constructing a class of estimators improving on the James-Stein estimator is given based on the integral expression of the risk.

• 日本統計学会の論文
• 2009-12-01

著者

• Kubokawa Tatsuya

  Faculty Of Economics University Of Tokyo

関連論文

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• PREDICTION IN MULTIVARIATE MIXED LINEAR MODELS
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• ESTIMATION OF VARIANCE AND COVARIANCE COMPONENTS IN ELLIPTICALLY CONTOURED DISTRIBUTIONS
• CHARACTERIZATION OF PRIORS IN THE STEIN PROBLEM
• INTEGRAL INEQUALITY FOR MINIMAXITY IN THE STEIN PROBLEM
• Estimation of EPMC for High-dimensional Data(Session 3b)

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