Progressive censoring schemes for marshall-olkin pareto distribution with applications: Estimation and prediction.

ABSTRACT: In this paper two prediction methods are used to predict the non-observed (censored) units under progressive Type-II censored samples. The lifetimes of the units follow Marshall-Olkin Pareto distribution. We observe the posterior predictive density of the non-observed units and construct predictive intervals as well. Furthermore, we provide inference on the unknown parameters of the Marshall-Olkin model, so we observe point and interval estimation by using maximum likelihood and Bayesian estimation methods. Bayes estimation methods are obtained under quadratic loss function. EM algorithm is used to obtain numerical values of the Maximum likelihood method and Gibbs and the Monte Carlo Markov chain techniques are utilized for Bayesian calculations. A simulation study is performed to evaluate

SUBMITTER: Alshenawy R 

PROVIDER: S-EPMC9328570 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature