The New Exponentiated Half Logistic-Harris-G Family of Distributions with Actuarial Measures and Applications

In this study, we introduce a new generalized family of distributions called the Exponentiated Half Logistic-Harris-G (EHL-Harris-G) distribution, which extends Harris-G distribution. The motivation for introducing lies in its ability to overcome limitations previous families, enhance flexibility, improve tail behavior, provide better statistical properties and find applications several fields. Several properties, including hazard rate function, quantile moments, moments residual life, distribution order statistics Rényi entropy are discussed. Risk measures, such as value at risk, variance premium, also derived studied. To estimate parameters EHL-Harris-G distributions, following six different estimation approaches used: maximum likelihood (MLE), least-squares (LS), weighted (WLS), product spacing (MPS), Cramér–von Mises (CVM), Anderson–Darling (AD). Monte Carlo simulation results EHL-Harris-Weibull (EHL-Harris-W) show that MLE method allows us obtain estimates, followed by WLS then AD. Finally, EHL-Harris-W is superior some other equi-parameter non-nested models literature, fitting it two real-life data sets from disciplines.