title: A 3-component mixture of Rayleigh distributions: properties and estimation in Bayesian framework.
abstract: To study lifetimes of certain engineering processes, a lifetime model which can accommodate the nature of such processes is desired. The mixture models of underlying lifetime distributions are intuitively more appropriate and appealing to model the heterogeneous nature of process as compared to simple models. This paper is about studying a 3-component mixture of the Rayleigh distributionsin Bayesian perspective. The censored sampling environment is considered due to its popularity in reliability theory and survival analysis. The expressions for the Bayes estimators and their posterior risks are derived under different scenarios. In case the case that no or little prior information is available, elicitation of hyperparameters is given. To examine, numerically, the performance of the Bayes estimators using non-informative and informative priors under different loss functions, we have simulated their statistical properties for different sample sizes and test termination times. In addition, to highlight the practical significance, an illustrative example based on a real-life engineering data is also given.
authors.affiliation: A Department of Basic Sciences, Riphah International University, Islamabad, 44000, Pakistan.
authors.forename: Muhammad
authors.initials: M
authors.lastname: Aslam
authors.affiliation: Department of Statistics, Quaid-i-Azam University, 45320, Islamabad, 44000, Pakistan.
authors.forename: Muhammad
authors.initials: M
authors.lastname: Tahir
authors.affiliation: Department of Statistics, Quaid-i-Azam University, 45320, Islamabad, 44000, Pakistan.
authors.forename: Zawar
authors.initials: Z
authors.lastname: Hussain
authors.affiliation: Department of Statistics, King Abdulaziz University, 21589, Jeddah, 80203, Saudi Arabia.
authors.forename: Bander
authors.initials: B
authors.lastname: Al-Zahrani
mesh_terms: D001499:Bayes Theorem; D015203:Reproducibility of Results; D018401:Sample Size; D016008:Statistical Distributions