On The Bayesian Analysis of Censored Mixture of Two Topp-Leone Distribution

This paper develops a Bayesian analysis in the context of non-informative priors for the shape parameter of the mixture of Topp-Leone using the censored data. A population of certain objects is assumed to be composed of two subgroups mixed together in an unknown proportion. The random observation taken from this population is supposed to be characterized by one of the two distinct unknown members of a Topp-Leone distribution. We model the heterogeneous population using two components mixture of the Topp-Leone distribution. A comprehensive simulation scheme has been carried out to highlight the properties and behavior of the estimates in terms of sample size, corresponding risks and the mixing weights. A censored mixture data is simulated by probabilistic mixing for the computational purpose. The Bayes estimators of the said parameters have been derived under the assumption of non-informative priors using different loss functions. Posterior risks of the Bayes estimators are compared to explore the effect of prior information and loss functions. Bayes estimators assuming the uniform prior have been observed performing better.