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. It is also shown that the conditions can be expressed based on the inverse Laplace transform of the general prior. The relationsip between Stein's super-harmonic condition and the general conditions is discussed. Finally, a characterization of the priors for the admissibility is given, and admissible and minimax estimators are developed.
内容記述
Revised in June 2006; subsequently published in Journal of the Japan Statistical Society (2007), 37, 207-237.
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雑誌名
Discussion paper series. CIRJE-F
巻
CIRJE-F-409
発行年
2006-03
書誌レコードID
AA11450569
フォーマット
application/pdf
日本十進分類法
330
出版者
日本経済国際共同センター
出版者別名
Center for International Research on the Japanese Economy
Characterization of Priors in the Stein Problem
