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Mustafa Nadar ;Revista Colombiana de Estadística 2015, 38 2

Autor: Fatih Kizilaslan

Fuente: http://www.redalyc.org/


Introducción



Revista Colombiana de Estadística ISSN: 0120-1751 revcoles_fcbog@unal.edu.co Universidad Nacional de Colombia Colombia Kizilaslan, Fatih; Nadar, Mustafa Classical and Bayesian Estimation of Reliability in Multicomponent Stress-Strength Model Based on Weibull Distribution Revista Colombiana de Estadística, vol.
38, núm.
2, julio, 2015, pp.
467-484 Universidad Nacional de Colombia Bogotá, Colombia Available in: http:--www.redalyc.org-articulo.oa?id=89940050010 How to cite Complete issue More information about this article Journals homepage in redalyc.org Scientific Information System Network of Scientific Journals from Latin America, the Caribbean, Spain and Portugal Non-profit academic project, developed under the open access initiative Revista Colombiana de Estadística July 2015, Volume 38, Issue 2, pp.
467 to 484 DOI: http:--dx.doi.org-10.15446-rce.v38n2.51674 Classical and Bayesian Estimation of Reliability in Multicomponent Stress-Strength Model Based on Weibull Distribution Estimación clásica y bayesiana de la confiabilidad de un modelo estrés-fuerza basado en la distribución Weibull Fatih Kizilaslan1,a , Mustafa Nadar2,b 1 Department of Mathematıcs, Faculty of Scıence, Gebze Technıcal Unıversıty, Kocaelı, Turkey 2 Departament of Mathematıcal Engıneerıng, Faculty of Arts and Scıences, Istanbul Technıcal Unıversıty, Istanbul, Turkey Abstract In this study, we consider a multicomponent system which has k independent and identical strength components X1 , .
, Xk and each component is exposed to a common random stress Y when the underlying distributions are Weibull.
The system is regarded as operating only if at least s out of k (1 ≤ s ≤ k) strength variables exceeds the random stress.
We estimate the reliability of the system by using frequentist and Bayesian approaches. The Bayes estimate of the reliability has been developed by using Lindley’s approximation and the Markov Chain Monte Carlo methods due to the lack of exp...





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