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

Manoj Chacko and Rakhi Mohan ()
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Manoj Chacko: University of Kerala, Trivandrum, India
Rakhi Mohan: University of Kerala, Trivandrum, India

Statistica, 2018, vol. 78, issue 3, 251-272

Abstract: 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.

Keywords: Gompertz distribution; Progressive type-II censoring; Binomial removals; Bayes estimates; MCMC method (search for similar items in EconPapers)
Date: 2018
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Persistent link: https://EconPapers.repec.org/RePEc:bot:rivsta:v:78:y:2018:i:3:p:251-272

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