Asymptotic Optimality of Three Stage Design for Estimating Product of Means with Applications in Reliability Estimation and Risk Assessment

Authors

  • Xing Song University of Missouri-Kansas City, Kansas City, MO 64110, United States
  • Kamel Rekab University of Missouri-Kansas City, Kansas City, MO 64110, United States

DOI:

https://doi.org/10.15377/2409-5761.2016.03.01.3

Keywords:

One-parameter exponential family, first-order optimality, three-stage sampling scheme, sequential design, applications, Beta-Binomial, Normal-Normal, system reliability, risk assessment, bayesian estimation

Abstract

The one-parameter exponential family is a practically convenient and widely used unified family of distributions, which contains both discrete and continuous distributions that can be used for practical modelling, such as the Binomial, Beta, Normal, etc. The problem of estimating product of means has been explored for independent populations from one-parameter exponential family in a general sense, with a three-stage sampling design proposed and proven to be first-order efficient. The purpose of this paper is to apply the theoretical results to specific applications and to provide practical guidance on implementing the proposed sequential design. One popular application problem of interest is to estimate the system reliability, for which a Beta-Binomial model will be adopted. The other practical problem, which is often encountered in environmental study, is risk assessment and a Normal-Normal model will be used for the case.

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Author Biographies

  • Xing Song, University of Missouri-Kansas City, Kansas City, MO 64110, United States
    Department of Mathematics and Statistics
  • Kamel Rekab, University of Missouri-Kansas City, Kansas City, MO 64110, United States
    Department of Mathematics and Statistics

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Published

2016-06-16

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How to Cite

Asymptotic Optimality of Three Stage Design for Estimating Product of Means with Applications in Reliability Estimation and Risk Assessment. (2016). Journal of Advances in Applied & Computational Mathematics, 3(1), 8-19. https://doi.org/10.15377/2409-5761.2016.03.01.3

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