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Bayes inference under a finite mixture of two-compound Gompertz components model

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This paper consists of two parts: the first part covers the Bayes estimation of the parameters of a hetrogeneous population represented by a finite mixture of two compound Gompertz components. An objective indifferent prior is assumed and the Markov Chain Monte Carlo algorithm is used in the computations of the estimates. In the second part, Bayes prediction of future observables, based on a two-sample scheme is developed.
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Keywords: Compound Gompertz distribution; Estimation; Finite mixture; Heterogeneous population; Markov Chain Monte Carlo; Prediction; Two-sample scheme; Type I censoring

Document Type: Research Article

Affiliations: Faculty of Science, Department of Statistics and Operations Research, Kuwait University, Safat-13060, Kuwait

Publication date: 01 January 2007

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