Open AccessEstimation of the Inverse Weibull Distribution Parameters under Type-I Hybrid Censoring
Author(s) -
Mohammad Kazemi,
Mina Azizpoor
Publication year - 2021
Publication title -
österreichische zeitschrift für statistik
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.342
H-Index - 9
ISSN - 1026-597X
DOI - 10.17713/ajs.v50i5.1134
Subject(s) - censoring (clinical trials) , weibull distribution , statistics , mathematics , markov chain monte carlo , bayes' theorem , prior probability , bayes factor , gibbs sampling , bayesian probability
The hybrid censoring is a mixture of type-I and type-II censoring schemes. This paper presents the statistical inferences of the inverse Weibull distribution parameters when the data are type-I hybrid censored. First, we consider the maximum likelihood estimates of the unknown parameters. It is observed that the maximum likelihood estimates can not be obtained in closed form. We further obtain the Bayes estimates and the corresponding highest posterior density credible intervals of the unknown parameters under the assumption of independent gamma priors using the importance sampling procedure. We also compute the approximate Bayes estimates using Lindley's approximation technique. The performance of the Bayes estimates have been compared with maximum likelihood estimates through the Monte Carlo Markov chain techniques. Finally, a real data set have been analysed for illustration purpose.