Estimation in Inverse Weibull Distribution Based on Randomly Censored Data
DOI:
https://doi.org/10.6092/issn.1973-2201/8414Keywords:
Random censoring, Inverse Weibull distribution, Maximum likelihood estimation, Expected Fisher information, Expected time on test, MCMC techniqueAbstract
This article deals with the estimation of the parameters and reliability characteristics in inverse Weibull (IW) distribution based on the random censoring model. The censoring distribution is also taken as an IW distribution. Maximum likelihood estimators of the parameters, survival and failure rate functions are derived. Asymptotic confidence intervals of the parameters based on the Fisher information matrix are constructed. Bayes estimators of the parameters, survival and failure rate functions under squared error loss function using non-informative and gamma informative priors are developed. Furthermore, Bayes estimates are obtained using Tierney-Kadane's approximation method and Markov chain Monte Carlo (MCMC) techniques. Also, highest posterior density (HPD) credible intervals of the parameters based on MCMC techniques are constructed. A simulation study is conducted to compare the performance of various estimates. Finally, a randomly censored real data set supports the estimation procedures developed in this article.
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