On Reliability in a Multicomponent Stress-Strength Model with Power Lindley Distribution

In this paper  we study the reliability of a multicomponent stress-strength model assuming that the components follow power Lindley model.  The maximum likelihood estimate of the reliability parameter and its asymptotic confidence interval are obtained. Applying the parametric Bootstrap technique, interval estimation of the reliability is presented.  Also, the Bayes estimate and highest posterior density credible interval of the reliability parameter are derived using suitable priors on the parameters. Because there is no closed form for the Bayes estimate, we use the Markov Chain Monte Carlo method to obtain approximate Bayes  estimate of the reliability. To evaluate the performances of different procedures,  simulation studies are conducted and an example of real data sets is provided.

• Autores: Pak, Abbas; Gupta, Arjun Kumar; Khoolenjani, Nayereh Bagheri
• Tipo de recurso: Article of journal
• Fecha de publicación: 2018
• Institución: Universidad Nacional de Colombia
• Repositorio: Universidad Nacional de Colombia
• Idioma: spa
• OAI Identifier: oai:repositorio.unal.edu.co:unal/66488
• Acceso en línea: https://repositorio.unal.edu.co/handle/unal/66488; http://bdigital.unal.edu.co/67516/
• Palabra clave: 51 Matemáticas / Mathematics; 31 Colecciones de estadística general / Statistics; Inferencia bayesiana; intervalo de conanza Bootstrap; estimaci ón de máxima verosimilitud; modelo de resistencia al estrés; Bayesian inference; Bootstrap condence interval; Maximum likelihood estimation; Stress-strength model
• Rights: openAccess
• License: Atribución-NoComercial 4.0 Internacional