Estimation a Stress-Strength Model for P(Yr: n_1< Xk:n_2) Using the Lindley Distribution

• MARWA KH HASSAN ^[1]
  1. [1] Ain Shams University

     Ain Shams University

     Egipto

     
• Localización: Revista Colombiana de Estadística, ISSN-e 2389-8976, ISSN 0120-1751, Vol. 40, Nº. 1, 2017, págs. 105-121
• Idioma: inglés
• DOI: 10.15446/rce.v40n1.54349
• Títulos paralelos:
  • En estimación del estrés fuerza modelo en la caja P(Yr: n_1< Xk:n_2) de distribución Lindley
• Enlaces
  • Texto completo
• Resumen
  • español

    El problema de la fiabilidad de estimación en el modelo de estrés-fuerza multicomponente, cuando el sistema consta de componentes k tiene fuerza, cada componente experimentando un estrés al azar se considera en este documento. Se obtiene la fiabilidad de estos sistemas cuando las variables de fuerza y tensión están dadas por la distribución Lindley. El sistema es considerado como vivo solo si al menos r de k (r < k) fuerzas superan el estrés. La fiabilidad de varios componentes del sistema viene dado por Rr, k . El estimador de máxima verosimilitud (MLE), se obtienen estimadores insesgados de varianza uniformemente mínima (UMVUE) y el estimador de Bayes Rr, k . Se realizó un estudio de simulación para comparar los diferentes estimadores de Rr, k . Se utilizaron datos reales como aplicación práctica para el modelo propuesto.

  • English

    The problem of estimation reliability in a multicomponent stress-strength model, when the system consists of k components have strength each component experiencing a random stress, is considered in this paper. The reliability of such a system is obtained when strength and stress variables are given by Lindley distribution. The system is regarded as alive only if at least r out of k (r< k) strength exceeds the stress. The multicomponent reliability of the system is given by Rr,k. The maximum likelihood estimator (MLE), uniformly minimum variance unbiased estimator (UMVUE) and Bayes estimator of Rr,k are obtained. A simulation study is performed to compare the different estimators of Rr,k. Real data is used as a practical application of the proposed model.

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