Partitioning bernoulli populations with respect to a control

Elena M. Buzaianu; Pinyuen Chen

doi:10.1080/03610910902936315

Partitioning bernoulli populations with respect to a control

Elena M. Buzaianu, Pinyuen Chen

Department of Mathematics

Research output: Contribution to journal › Article › peer-review

Abstract

This article is concerned with the problem of partitioning a set of Bernoulli populations with respect to a control Bernoulli population, into two subsets according to their probabilities of success in a single trial. Two procedures are proposed. The first one is a single-stage procedure, based on Tong's single-stage procedure (1969) for partitioning normal populations. The second procedure is a two-stage procedure with elimination, based on Solanky's two-stage procedure (2006) for normal populations. The derivation of the probability of a correct decision and the least favorable configurations are exact, without making use of the normal approximation to the binomial.

Original language	English (US)
Pages (from-to)	2769-2783
Number of pages	15
Journal	Communications in Statistics - Theory and Methods
Volume	38
Issue number	16-17
DOIs	https://doi.org/10.1080/03610910902936315
State	Published - Jan 2009

Keywords

Bernoulli population
Binomial distribution
Least favorable configuration
Probability of a correct decision
Sample size

ASJC Scopus subject areas

Statistics and Probability

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10.1080/03610910902936315

Cite this

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title = "Partitioning bernoulli populations with respect to a control",

abstract = "This article is concerned with the problem of partitioning a set of Bernoulli populations with respect to a control Bernoulli population, into two subsets according to their probabilities of success in a single trial. Two procedures are proposed. The first one is a single-stage procedure, based on Tong's single-stage procedure (1969) for partitioning normal populations. The second procedure is a two-stage procedure with elimination, based on Solanky's two-stage procedure (2006) for normal populations. The derivation of the probability of a correct decision and the least favorable configurations are exact, without making use of the normal approximation to the binomial.",

keywords = "Bernoulli population, Binomial distribution, Least favorable configuration, Probability of a correct decision, Sample size",

author = "Buzaianu, {Elena M.} and Pinyuen Chen",

note = "Funding Information: The authors wish to thank the Editor and referees for their helpful comments that led to a considerable improvement of this article. This research was supported in part by a research grant that the first author received from the University of North Florida. Copyright: Copyright 2009 Elsevier B.V., All rights reserved.",

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AU - Chen, Pinyuen

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N2 - This article is concerned with the problem of partitioning a set of Bernoulli populations with respect to a control Bernoulli population, into two subsets according to their probabilities of success in a single trial. Two procedures are proposed. The first one is a single-stage procedure, based on Tong's single-stage procedure (1969) for partitioning normal populations. The second procedure is a two-stage procedure with elimination, based on Solanky's two-stage procedure (2006) for normal populations. The derivation of the probability of a correct decision and the least favorable configurations are exact, without making use of the normal approximation to the binomial.

AB - This article is concerned with the problem of partitioning a set of Bernoulli populations with respect to a control Bernoulli population, into two subsets according to their probabilities of success in a single trial. Two procedures are proposed. The first one is a single-stage procedure, based on Tong's single-stage procedure (1969) for partitioning normal populations. The second procedure is a two-stage procedure with elimination, based on Solanky's two-stage procedure (2006) for normal populations. The derivation of the probability of a correct decision and the least favorable configurations are exact, without making use of the normal approximation to the binomial.

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KW - Binomial distribution

KW - Least favorable configuration

KW - Probability of a correct decision

KW - Sample size

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Partitioning bernoulli populations with respect to a control

Abstract

Keywords

ASJC Scopus subject areas

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