CHARACTERIZATION OF PRIORS IN THE STEIN PROBLEM

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概要

• 論文の詳細を見る

• The so-called Stein problem is addressed in the estimation of a mean vector of a multivariate normal distribution with a known covariance matrix. For general prior distributions with sphericity, the paper derives conditions on priors under which the resulting generalized Bayes estimators are minimax relative to the usual quadratic loss. It is also shown that the conditions can be expressed based on the inverse Laplace transform of the general prior. Stein's super-harmonic condition is derived from the general conditions. Finally, the priors are characterized for the admissibility.

• 一般社団法人日本統計学会の論文

著者

• Kubokawa Tatsuya

  Faculty Of Economics University Of Tokyo

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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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