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dc.contributor.advisorRekab, Kamel
dc.contributor.advisorMedhi, Deepankar
dc.contributor.authorXia, Xing
dc.date.issued2019
dc.date.submitted2019 Fall
dc.descriptionTitle from PDF of title page viewed January 8, 2020
dc.descriptionDissertation advisor: Kamel Rekab and Deep Medhi,
dc.descriptionVita
dc.descriptionIncludes bibliographical references (page 60-62)
dc.descriptionThesis (Ph.D.)--Department of Mathematics and Statistics, School of Computing and Engineering. University of Missouri--Kansas City, 2019
dc.description.abstractIn order to estimate the reliability of sequentially designed procedures under the Bayesian framework with conjugate priors, a sharp lower bound for the Bayes risk has been derived. Chapter 1 and 2 introduce the background and fundamental concepts and theorems of this study. Chapter 3 focuses on deriving second-order efficiency of Bayes risk for two independent components in the one-parameter exponential family which includes the most common distribution in application of reliability testing, Bernoulli distribution. Chapter 3 also uses Monte Carlo simulations with several proposed sequential designs to illustrate optimality of the second-order efficiency. Then Chapter 4 extends the result to k (k>2) independent components sequentially designed systems. The same Monte Carlo simulations were performed to assure that the second order lower bound is achieved.
dc.description.tableofcontentsIntroduction -- Conceptual framework -- Second-order efficiency for estimating product of 2 components in exponential family -- Second-order efficiency for estimating product of K components -- Conclusion
dc.format.extentxi, 63 pages
dc.identifier.urihttps://hdl.handle.net/10355/70891
dc.subject.lcshSequential processing (Computer science)
dc.subject.lcshBayesian statistical decision theory
dc.subject.otherDissertation -- University of Missouri--Kansas City -- Mathematics
dc.subject.otherDissertation -- University of Missouri--Kansas City -- Computer science
dc.titleEfficient sequential designs with asymptotic second-order lower bound of Bayes risk for estimating product of means
thesis.degree.disciplineMathematics (UMKC)
thesis.degree.disciplineTelecommunication and Computer Networking (UMKC)
thesis.degree.grantorUniversity of Missouri--Kansas City
thesis.degree.levelPh.D.
thesis.degree.levelDoctoral
thesis.degree.namePh.D. (Doctor of Philosophy)


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