TY  - EJOU
AU  - Fayomi, Aisha 
AU  - Chesneau, Christophe 
AU  - Jamal, Farrukh 
AU  - Algarni, Ali 

TI  - On a Novel Extended Lomax Distribution with Asymmetric Properties and Its Statistical Applications
T2  - Computer Modeling in Engineering \& Sciences

PY  - 2023
VL  - 136
IS  - 3
SN  - 1526-1506

AB  - 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 modeling horizon beyond that of the Lomax distribution. In the first
part, we examine several of its mathematical properties, such as the shapes of the related probability and hazard
rate functions; stochastic comparisons; manageable expansions for various moments; and quantile properties.
In particular, based on the quantile functions, various actuarial measures are discussed. In the second part, the
distribution’s applicability is investigated with the use of the maximum likelihood estimation method. The behavior
of the obtained parameter estimates is validated by a simulation work. Insurance claim data are analyzed. We show
that the proposed distribution outperforms eight well-known distributions, including the Lomax distribution and
several extended Lomax distributions. In addition, we demonstrate that it gives preferable inferences from these
competitor distributions in terms of risk measures.
KW  - Lomax distribution; extended Lomax distribution; asymmetry; actuarial measures; maximum likelihood estimation; data analysis

DO  - 10.32604/cmes.2023.027000