Bayesian Inference and Data Analysis of the Unit-Power Burr X Distribution

被引：23

作者：

Fayomi, Aisha ^{[1
]}

Hassan, Amal S. ^{[2
]}

Baaqeel, Hanan ^{[1
]}

Almetwally, Ehab M. ^{[3
]}

机构：

[1] King Abdulaziz Univ, Fac Sci, Dept Stat, Jeddah 21589, Saudi Arabia

[2] Cairo Univ, Fac Grad Studies Stat Res FGSSR, Giza 12613, Egypt

[3] Delta Univ Sci & Technol, Fac Business Adm, Gamasa 11152, Egypt

来源：

AXIOMS | 2023年 / 12卷 / 03期

关键词：

power Burr X distribution; entropy; Bayesian estimation; Metropolis-Hastings; COVID-19; data; LINDLEY DISTRIBUTION; REGRESSION-MODEL; GENERATOR;

D O I：

10.3390/axioms12030297

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

The unit-power Burr X distribution (UPBXD), a bounded version of the power Burr X distribution, is presented. The UPBXD is produced through the inverse exponential transformation of the power Burr X distribution, which is also beneficial for modelling data on the unit interval. Comprehensive analysis of its key characteristics is performed, including shape analysis of the primary functions, analytical expression for moments, quantile function, incomplete moments, stochastic ordering, and stress-strength reliability. Renyi, Havrda and Charvat, and d-generalized entropies, which are measures of uncertainty, are also obtained. The model's parameters are estimated using a Bayesian estimation approach via symmetric and asymmetric loss functions. The Bayesian credible intervals are constructed based on the marginal posterior distribution. Monte Carlo simulation research is intended to test the accuracy of various estimators based on certain measures, in accordance with the complex forms of Bayesian estimators. Finally, we show that the new distribution is more appropriate than certain other competing models, according to their application for COVID-19 in Saudi Arabia and the United Kingdom.

引用

页数：26