Abstract
This article presents the procedures for the estimation of the parameter of Rayleighdistribution based on Type-II progressive hybrid censored fuzzy lifetime data. Classicalas well as the Bayesian procedures for the estimation of unknown model parameters has been developed. The estimators obtained here are Maximum likelihood (ML) estimator, Method of moments (MM) estimator, Computational approach (CA) estimator and Bayes estimator. Highest posterior density (HPD) credible intervals of the unknown parameter are obtained by using Markov Chain Monte Carlo (MCMC) technique. For numerical illustration, a real data set has been considered.
Highlights
In a life testing and reliability experiment, several items are put on test and the experiment is terminated when all of them fail to work
We have considered the problem of point estimation of the parameter of Rayleigh distribution since it is a popular model being used in different branches of science and engineering
We have found that Bayesian procedure provides best estimate of the unknown parameter of the Rayleigh model with the smallest mean squared error (MSE) among all the four estimators and it is followed by the MLE, MME and computational approach estimation (CAE)
Summary
In a life testing and reliability experiment, several items are put on test and the experiment is terminated when all of them fail to work. This process is often very time-consuming and costly. Units may be taken out of the study before completion of experiment because it is damaged due to some reasons or lack of money compels to terminate the experiment prior to its completion Data obtained from such experiments are called censored data because in such cases one is not completely ignorant about the lifetime of the censored items, in the sense that a partial information about their lifetime is known. In present day scenario, censoring is a need to reduce total time and cost associated with the life-testing experiment
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