A posterior probability base sequential binormal test method for verification of success ratio P

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A posterior probability base sequential binormal test method for verification of success ratio P. / Liu, Qi; An, Min.

In: World Journal of Engineering, Vol. 8, No. 4, 27.12.2011, p. 313-324.

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@article{f2a46526ee7a427db5818a24ea1e89eb,
title = "A posterior probability base sequential binormal test method for verification of success ratio P",
abstract = "Testing the lifetimes of components or products by means of the verification of probability of success ratio p on which to base a statistical probability of the binomial distribution is often a costly and difficult undertaking. Sometimes tests cannot reach at a desirable target, particularly, in the reliability assurance tests. A Bayesian sequential binomial test model (BSBTM) is proposed for obtaining the composite hypothesis of p, which posterior criteria are taken into consideration. In order to get robust decision criteria and closed continuation-sampling regions, a modified Bayesian sequential binomial test model (MBSBTM) is also developed. By using BSBTM and MBSBTM, the upper and lower boundaries of a continuation-sampling region can be determined and the decision criteria can be made. A simulation method of calculating the average sample number (ASN) by using MBSBTM is also presented in this paper. Two case examples are used to demonstrate the proposed BSBTM and MBSBTM methodologies. The results indicate that by using MBSBTM sample numbers can be decided effectively and efficiently in the reliability tests.{\textcopyright} 2011 Muilt-Science Inc. All rights reserved.",
keywords = "Bayesian, BSBTM, MBSBTM, sequential test, Posterior probability",
author = "Qi Liu and Min An",
year = "2011",
month = dec
day = "27",
doi = "10.1260/1708-5284.8.4.313",
language = "English",
volume = "8",
pages = "313--324",
journal = "World Journal of Engineering",
issn = "1708-5284",
publisher = "Multi-Science Publishing",
number = "4",

}

RIS

TY - JOUR

T1 - A posterior probability base sequential binormal test method for verification of success ratio P

AU - Liu, Qi

AU - An, Min

PY - 2011/12/27

Y1 - 2011/12/27

N2 - Testing the lifetimes of components or products by means of the verification of probability of success ratio p on which to base a statistical probability of the binomial distribution is often a costly and difficult undertaking. Sometimes tests cannot reach at a desirable target, particularly, in the reliability assurance tests. A Bayesian sequential binomial test model (BSBTM) is proposed for obtaining the composite hypothesis of p, which posterior criteria are taken into consideration. In order to get robust decision criteria and closed continuation-sampling regions, a modified Bayesian sequential binomial test model (MBSBTM) is also developed. By using BSBTM and MBSBTM, the upper and lower boundaries of a continuation-sampling region can be determined and the decision criteria can be made. A simulation method of calculating the average sample number (ASN) by using MBSBTM is also presented in this paper. Two case examples are used to demonstrate the proposed BSBTM and MBSBTM methodologies. The results indicate that by using MBSBTM sample numbers can be decided effectively and efficiently in the reliability tests.© 2011 Muilt-Science Inc. All rights reserved.

AB - Testing the lifetimes of components or products by means of the verification of probability of success ratio p on which to base a statistical probability of the binomial distribution is often a costly and difficult undertaking. Sometimes tests cannot reach at a desirable target, particularly, in the reliability assurance tests. A Bayesian sequential binomial test model (BSBTM) is proposed for obtaining the composite hypothesis of p, which posterior criteria are taken into consideration. In order to get robust decision criteria and closed continuation-sampling regions, a modified Bayesian sequential binomial test model (MBSBTM) is also developed. By using BSBTM and MBSBTM, the upper and lower boundaries of a continuation-sampling region can be determined and the decision criteria can be made. A simulation method of calculating the average sample number (ASN) by using MBSBTM is also presented in this paper. Two case examples are used to demonstrate the proposed BSBTM and MBSBTM methodologies. The results indicate that by using MBSBTM sample numbers can be decided effectively and efficiently in the reliability tests.© 2011 Muilt-Science Inc. All rights reserved.

KW - Bayesian

KW - BSBTM

KW - MBSBTM

KW - sequential test

KW - Posterior probability

U2 - 10.1260/1708-5284.8.4.313

DO - 10.1260/1708-5284.8.4.313

M3 - Article

VL - 8

SP - 313

EP - 324

JO - World Journal of Engineering

JF - World Journal of Engineering

SN - 1708-5284

IS - 4

ER -