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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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