Open AccessBayesian inference for Rayleigh Pareto distribution under progressively type-II right censored data
Author(s) -
Assia Chadli,
Sara Kermoune
Publication year - 2021
Publication title -
pakistan journal of statistics and operation research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.354
H-Index - 15
eISSN - 2220-5810
pISSN - 1816-2711
DOI - 10.18187/pjsor.v17i3.3695
Subject(s) - mathematics , pareto distribution , estimator , rayleigh distribution , bayes estimator , lomax distribution , statistics , bayes factor , bayesian inference , bayesian probability , pareto interpolation , bayesian linear regression , inference , generalized pareto distribution , computer science , probability density function , extreme value theory , artificial intelligence
In this paper, we consider inference problems including estimation for a Rayleigh Pareto (RP) distribution under progressively type-II right censored data. We use two approaches, the classical maximum likelihood approach and the Bayesian approach for estimating the distribution parameters and the reliability characteristics. Bayes estimators and corresponding posterior risks (PR) have been derived using different loss functions (symmetric and asymmetric). The estimators cannot be obtained explicitly, so we use the method of Monte Carlo. Finally, we use the integrated mean square error (IMSE) and the Pitman closeness criterion to compare the results of the two methods.