Multiple-step stress accelerated life for Weibull Poisson distribution with type I censoring

Abstract

This article presents multi-step stress accelerated life tests based on type I censoring. It is assumed that the lifetime at design stress has the Weibull Poisson distribution. The scale parameter of the Weibull Poisson failure time at constant levels is assumed to have an inverse power law of the stress levels. Under the assumption of a cumulative exposure model, the maximum likelihood method is used to obtain the estimators of the model parameters. The optimal design of the accelerated life tests is studied according to the A-optimality criterion to specify the optimal stress change time and the optimal censoring time. Finally, the numerical studies are performed to illustrate the proposed procedures.

Sağkalım ve Güvenilirlik Analizlerinde Yeni Bir Olasılık Dağılımı

“…Bu tanımlamada kullanılan ek parametre (ler), kuyruk özelliklerini temsil edebilmek ve üretici olasılık dağılım ailesinin uyum iyiliğini iyileştirmek için kullanılmaktadır. Bu çalışmaların bazıları: Marshall-Olkin dağılım ailesi (MO-G) [1], beta-G ailesi [2], gamma-G dağılım ailesi [3], exponentiated T-X dağılım ailesi [4], Kumaraswamy-G dağılım ailesi (Kw-G) [5], transformed-transformer (T-X) [6], additive Weibull-G dağılım ailesi [7], Lindley-G dağılım ailesi Cakmakyapan [8], odd Lindley-G dağılım ailesi [9], Alpha Power dönüşümü ailesi [10], Frechet Topp Leone-G dağılım ailesi [11].…”

E-Bayesian estimation of the shape parameter of Lomax model under symmetric and asymmetric loss functions

“…Orginally, it was introduced for modelling of business failure data, size of cities, reliability modelling, and lifetime testing in engineering as well as in survival analysis. Hassan and Al-Ghamdi (2009) [13] used Lomax distribution for determination of optimal times of changing level of stress for simple stress plans under a cumulative exposure model. Ahmad et al (2015) [4] obtained the Bayes estimators of the shape parameters of the Lomax distribution by employing the Jeffery's and extension of Jeffery's prior using Al-Bayyati's loss function, squared error loss function, and precautionary loss function.…”