Estimation in Weibull Distribution Under Progressively Typ e-I Hybrid Censored Data

Abstract

In this article, we consider the estimation of unknown parameters of Weibull distribution when the lifetime data are observed in the presence of progressively typ e-I hybrid censoring scheme. The Newton-Raphson algorithm, Expectation-Maximization (EM) algorithm and Stochastic EM algorithm are utilized to derive the maximum likelihood estimates for the unknown parameters. Moreover, Bayesian estimators using Tierney-Kadane Method and Markov Chain Monte Carlo method are obtained under three different loss functions, namely, squared error loss, linear-exponential and generalized entropy loss functions. Also, the shrinkage pre-test estimators are derived. An extensive Monte Carlo simulation experiment is conducted under different schemes so that the performances of the listed estimators are compared using mean squared error, confidence interval length and coverage probabilities. Asymptotic normality and MCMC samples are used to obtain the confidence intervals and highest posterior density intervals respectively. Further, a real data example is presented to illustrate the methods. Finally, some conclusive remarks are presented.

Keywords

Bayesian estimation; EM algorithm; SEM algorithm; Tierney-Kadane's approximation; progressively type-I hybrid censoring; Weibull distribution

Published in

Revstat Statistical Journal
2022, volume: 20, number: 5, pages: 563-586
Publisher: INST NACIONAL ESTATISTICA-INE

SLU Authors

• Belaghi, Reza

  • Department of Energy and Technology, Swedish University of Agricultural Sciences
    

UKÄ Subject classification

Probability Theory and Statistics

Publication identifier

• DOI: https://doi.org/10.57805/revstat.v20i5.389

Permanent link to this page (URI)

https://res.slu.se/id/publ/126930