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on the stochastic minimization of sample size by the bechhofer-kulkarni bernoulli sequential selection procedure

 

作者: C. Jennison,  

 

期刊: Sequential Analysis  (Taylor Available online 1989)
卷期: Volume 8, issue 3  

页码: 281-291

 

ISSN:0747-4946

 

年代: 1989

 

DOI:10.1080/07474948908836182

 

出版商: Marcel Dekker, Inc.

 

关键词: k-population;Bernoulli selection problem;sequential selection procedure;adaptive sampling

 

数据来源: Taylor

 

摘要:

Bechhofer and Kulkarni (1982) proposed procedures for selecting that one ofkBernoulli populations with the largest single trial success probability. They showed that their procedure fork= 2 minimizes the expected total sample size amongst a class of procedures, all of which attain the same probability of correct selection. Kulkarni and Jennison (1986) generalized this result to the casek≥ 3. In this article we prove the stronger result that the Bechhofer-Kulkarni procedure for eachk≥2 stochastically minimizes the distribution of sample size amongst procedures in the same class. That is, the distribution of sample size for the Bechhofer-Kulkarni procedure is the same as or stochastically smaller than that for any other procedure in the class.

 

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