Aisha Fayomi¹, Christophe Chesneau^2,*, Farrukh Jamal³, Ali Algarni¹

CMES-Computer Modeling in Engineering & Sciences, Vol.136, No.3, pp. 2371-2403, 2023, DOI:10.32604/cmes.2023.027000

Abstract In this article, we highlight a new three-parameter heavy-tailed lifetime distribution that aims to extend the modeling possibilities of the Lomax distribution. It is called the extended Lomax distribution. The considered distribution naturally appears as the distribution of a transformation of a random variable following the logweighted power distribution recently introduced for percentage or proportion data analysis purposes. As a result, its cumulative distribution has the same functional basis as that of the Lomax distribution, but with a novel special logarithmic term depending on several parameters. The modulation of this logarithmic term reveals new types of asymetrical shapes, implying a… More >

Muhammad Arif¹, Dost Muhammad Khan¹, Saima Khan Khosa², Muhammad Aamir¹, Adnan Aslam³, Zubair Ahmad⁴, Wei Gao^5,*

CMC-Computers, Materials & Continua, Vol.66, No.1, pp. 537-550, 2021, DOI:10.32604/cmc.2020.012420

Abstract The actuaries always look for heavy-tailed distributions to model data relevant to business and actuarial risk issues. In this article, we introduce a new class of heavy-tailed distributions useful for modeling data in financial sciences. A specific sub-model form of our suggested family, named as a new extended heavy-tailed Weibull distribution is examined in detail. Some basic characterizations, including quantile function and raw moments have been derived. The estimates of the unknown parameters of the new model are obtained via the maximum likelihood estimation method. To judge the performance of the maximum likelihood estimators, a simulation analysis is performed in… More >