Statistical Inference for Kumaraswamy Distribution under Generalized Progressive Hybrid Censoring Scheme with Application  

作　　者：Magdy Nagy 

机构地区：[1]Department of Statistics and Operations Research,College of Science,King Saud University,Riyadh,11451,Saudi Arabia

出　　处：《Computer Modeling in Engineering & Sciences》2025年第4期185-223,共39页工程与科学中的计算机建模(英文)

基　　金：funded by Researchers Supporting Project number(RSPD2025R969),King Saud University,Riyadh,Saudi Arabia.

摘　　要：In this present work,we propose the expected Bayesian and hierarchical Bayesian approaches to estimate the shape parameter and hazard rate under a generalized progressive hybrid censoring scheme for the Kumaraswamy distribution.These estimates have been obtained using gamma priors based on various loss functions such as squared error,entropy,weighted balance,and minimum expected loss functions.An investigation is carried out using Monte Carlo simulation to evaluate the effectiveness of the suggested estimators.The simulation provides a quantitative assessment of the estimates accuracy and efficiency under various conditions by comparing them in terms of mean squared error.Additionally,the monthly water capacity of the Shasta reservoir is examined to offer real-world examples of how the suggested estimations may be used and performed.

关 键 词：Bayesian estimation E-Bayesian estimation H-Bayesian estimation generalized progressive hybrid Kumaraswamy distribution censoring sample maximum likelihood estimation 

分 类 号：O212[理学—概率论与数理统计]