Positivity of cumulative sums for multi-index function components explains the lower bound formula in the Levin-Robbins-Leu family of sequential subset selection procedures

IF 0.6 4区 数学 Q4 STATISTICS & PROBABILITY
B. Levin, C. Leu
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引用次数: 2

Abstract

Abstract We exhibit some strong positivity properties of a certain function that implies a key inequality that in turn implies the lower bound formula for the probability of correct selection in the Levin-Robbins-Leu family of sequential subset selection procedures for binary outcomes. These properties provide a more direct and comprehensive demonstration of the key inequality than was discussed in Levin and Leu (2013a).
多指标函数分量累积和的正性解释了序列子集选择过程的Levin-Robbins-Leu族中的下界公式
摘要我们证明了一个函数的一些强正性性质,该函数蕴涵了一个关键不等式,进而蕴涵了二元结果序列子集选择过程的Levin-Robbins-Leu族中正确选择概率的下界公式。这些性质比Levin和Leu (2013a)中讨论的更直接、更全面地展示了关键不等式。
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来源期刊
CiteScore
1.40
自引率
12.50%
发文量
20
期刊介绍: The purpose of Sequential Analysis is to contribute to theoretical and applied aspects of sequential methodologies in all areas of statistical science. Published papers highlight the development of new and important sequential approaches. Interdisciplinary articles that emphasize the methodology of practical value to applied researchers and statistical consultants are highly encouraged. Papers that cover contemporary areas of applications including animal abundance, bioequivalence, communication science, computer simulations, data mining, directional data, disease mapping, environmental sampling, genome, imaging, microarrays, networking, parallel processing, pest management, sonar detection, spatial statistics, tracking, and engineering are deemed especially important. Of particular value are expository review articles that critically synthesize broad-based statistical issues. Papers on case-studies are also considered. All papers are refereed.
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