Sequential procedures for selecting the best one ofkKoopman–Darmois populations
作者:
Edward Paulson,
期刊:
Sequential Analysis
(Taylor Available online 1994)
卷期:
Volume 13,
issue 3
页码: 207-220
ISSN:0747-4946
年代: 1994
DOI:10.1080/07474949408836305
出版商: Marcel Dekker, Inc.
关键词: sequential procedures;Koopman-Darmois populations;eliminating procedures
数据来源: Taylor
摘要:
By using a new approach for specifying the alternative distribution, we obtain a new eliminating procedure for selecting the best one ofkexperimental categories when the measurements from each category have a one-dimensional Koopman-Darmois distribution. When thekpopulations are normal with a common known variance, a modification of this approach results in a new closed eliminating procedure for selecting the population with the greatest mean. The Monte Carlo results summarized in Tables I and II indicate the new procedures lead to a reduction in the average total sample size when compared to the other available sequential procedures. We also obtain a sequential procedure for the case when the "best" experimental category is only preferred when it is better than a specified standard.
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