A Heavy Tailed Model Based on Power XLindley Distribution with Actuarial Data Applications  

作　　者：Mohammed Elgarhy Amal S.Hassan Najwan Alsadat Oluwafemi Samson Balogun Ahmed W.Shawki Ibrahim E.Ragab 

机构地区：[1]Department of Basic Sciences,Higher Institute of Administrative Sciences,Belbeis,44621,Egypt [2]Faculty of Graduate Studies for Statistical Research,Cairo University,Giza,12613,Egypt [3]Department of Quantitative Analysis,College of Business Administration,King Saud University,Riyadh,11587,Saudi Arabia [4]Department of Computing,University of Eastern Finland,Joensuu,80130,Finland [5]Central Agency for Public Mobilization&Statistics(CAPMAS),Cairo,11819,Egypt [6]Department of Basic Sciences,Egyptian Institute of Alexandria Academy for Management and Accounting,EIA,Alexandria,21919,Egypt

出　　处：《Computer Modeling in Engineering & Sciences》2025年第3期2547-2583,共37页工程与科学中的计算机建模(英文)

基　　金：supported by researchers Supporting Project Number(RSPD2025R548);King Saud University,Riyadh,Saudi Arabia.

摘　　要：Accurately modeling heavy-tailed data is critical across applied sciences,particularly in finance,medicine,and actuarial analysis.This work presents the heavy-tailed power XLindley distribution(HTPXLD),a unique heavy-tailed distribution.Adding one more parameter to the power XLindley distribution improves this new distribution,especially when modeling leptokurtic lifetime data.The suggested density provides greater flexibility with asymmetric forms and different degrees of peakedness.Its statistical features,like the quantile function,moments,extropy measures,incomplete moments,stochastic ordering,and stress-strength parameters,are explored.We further investigate its use in actuarial science through the computation of pertinent metrics,such as value-at-risk,tail value-at-risk,tail variance,and tail variance premium.To obtain the point and interval parameter estimates,we use the maximum likelihood estimation approach.We do many simulation tests to evaluate the performance of our proposed estimator.Metrics like bias,relative bias,mean squared error,root mean squared error,average interval length,and coverage probability will be used in these tests to assess the estimator’s performance.To illustrate the practical value of our proposed model,we apply it to analyze three real-world datasets.We then compare its performance to established competing models,highlighting its advantages.

关 键 词：Power XLindley heavy-tailed-G family extropy measure stochastic ordering parametric estimation asymmetric dataset 

分 类 号：O212[理学—概率论与数理统计]