Start-Up Demonstration Tests Under Markov Dependence

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Date

2010

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Isoss Publ

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Industrial Engineering
(1998)
Industrial Engineering is a field of engineering that develops and applies methods and techniques to design, implement, develop and improve systems comprising of humans, materials, machines, energy and funding. Our department was founded in 1998, and since then, has graduated hundreds of individuals who may compete nationally and internationally into professional life. Accredited by MÜDEK in 2014, our student-centered education continues. In addition to acquiring the knowledge necessary for every Industrial engineer, our students are able to gain professional experience in their desired fields of expertise with a wide array of elective courses, such as E-commerce and ERP, Reliability, Tabulation, or Industrial Engineering Applications in the Energy Sector. With dissertation projects fictionalized on solving real problems at real companies, our students gain experience in the sector, and a wide network of contacts. Our education is supported with ERASMUS programs. With the scientific studies of our competent academic staff published in internationally-renowned magazines, our department ranks with the bests among other universities. IESC, one of the most active student networks at our university, continues to organize extensive, and productive events every year.

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Abstract

A start-up demonstration test is a mechanism that can be used to demonstrate the reliability of an equipment to the customer. In this paper, we study CSTF (consecutive successes total failures) and TSTF (total successes total failures) procedures assuming that the individual start-ups follow a first order Markov dependence structure. Explicit (nonrecursive) expressions for the distributions of the test lengths are provided. Maximum likelihood and moments estimators of the expected test length are obtained and some numerical results are presented for an illustration.

Description

Eryilmaz, Serkan/0000-0002-2108-1781

Keywords

Start-up demonstration test, Maximum likelihood estimator, Markov chain

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Q3

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Volume

26

Issue

4

Start Page

637

End Page

647

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