STATISTICAL INFERENCE FOR THE GOMPERTZ DISTRIBUTION BASED ON PROGRESSIVE TYPE-II CENSORED DATA WITH BINOMIAL REMOVALS

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.