Statistical Inference for Gompertz Distribution based on Progressive Type-II Censored Data with Binomial Removals
Keywords:Gompertz distribution, Progressive type-II censoring, Binomial removals, Bayes estimates, MCMC method
In this paper, the problem of estimation of parameters for a two-parameterGompertz distribution is considered based on a progressively type-II censored sample with binomial removals. Together with the unknown parameters, the removal probability is also estimated. The maximum likelihood estimators of the parameters and the asymptotic variance-covariance matrix of the estimates are obtained. Bayes estimators are also obtained using different loss functions such as squared error, LINEX and general entropy. A simulation study is performed for comparison between various estimators developed in this paper. A real data set is also used for illustration.
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