Choice of Suitable Informative Prior for the Scale Parameter of Mixture of Laplace Distribution using Type-I Censoring Scheme under Different Loss Function

A comprehensive simulation scheme including a large number of parameter points is followed to highlight the properties and performance of the Bayes estimates and their posterior risk in terms of sample size, censoring rate and proportion of the component of the mixture using Levy and Gumbel Type-II informative priors. Limiting expressions for complete sample are also derived. The system of three non-linear equations, required to be solved iteratively for the computations of maximum likelihood (ML) estimates and predictive intervals are derived. A real-life mixture data application has been discussed. The Elicitation of hyperparameters of mixture through prior predictive approach has also argued. The Bayes estimates are evaluated under square error loss function, precautionary loss function, weighted squared error loss function and modified (quadratic) squared error loss function.