Asymptotically pointwise optimal rules of sequential estimation of mean vector when an information matrix has some structure in a multivariate normal population
作者:
Hisao Nagao,
期刊:
Sequential Analysis
(Taylor Available online 1997)
卷期:
Volume 16,
issue 4
页码: 363-374
ISSN:0747-4946
年代: 1997
DOI:10.1080/07474949708836394
出版商: Marcel Dekker, Inc.
关键词: A.P.O. rule;covariance matrix;conjugate distribution;optional stopping theorem;martingale;martingale convergence theorem;stopping rule;intraclass correlation structure;multivariate normal distribution
数据来源: Taylor
摘要:
The exact formulas of Bayes stopping times are often difficult to derive. Bickel and Yahav (1965) had provided the large sample approximation known as the "Asymptotically pointwise optimal" (A.P.O.) rule. The A.P.O. rule for the problem of the mean of a multivariate normal distribution, for a completely unknown covariance matrix , has been developed by the present author. This paper gives the A.P.O. rule of the mean of a multivariate normal ditribution for a covariance matrix with some structure. Also the result is shown to be asymptotically" non-deficient" in the sence of Woodroofe.
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