http://swrc.ontoware.org/ontology#TechnicalReport
Characterization of Priors in the Stein Problem
en
Admissibility
decision theory
estimation
generalized Bayes estimator
inverse Laplace transform
James-Stein estimator
minimaxity
risk function
shrinkage estimation
Stein problem
uniform domination
Kubokawa Tatsuya
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.
Revised in June 2006; subsequently published in Journal of the Japan Statistical Society (2007), 37, 207-237.
本文フィルはリンク先を参照のこと
Discussion paper series. CIRJE-F
CIRJE-F-409
2006-03
AA11450569
application/pdf
330
日本経済国際共同センター
http://www.cirje.e.u-tokyo.ac.jp/research/dp/2006/2006cf409ab.html