Preference of Prior for Two-Component Mixture of Lomax Distribution

Recently, El-Sherpieny et al (2020) suggested Type -II hybrid censoring method for parametric estimation of Lomax distribution (LD) without due regards being given to the choice priors and posterior risk associated with model. This paper fills this gap derived new LDmodel minimum posterior risk for selection priors. It derives a closed form expression Bayes estimates risks using Square error loss function (SELF), Weighted loss (WLF), Quadratic (QLF) Degroot (DLF). Prior predictive approach is used elicit hyper parameters mixture model. Analysis Bayes estimates presented in terms of sample size (n), mixing proportion ( p ) rate 0 t ), with the help simulation study. Usefulness model demonstrated on applying it simulated real-life data which show promising results of better reduction.