GOODNESS-OF-FIT OF NEW XLINDLEY DISTRIBUTION AND ITS APPLICATION IN MEDICINE

Abstract

Modeling and analyzing life expectancy data is essential in many fields, including actuarial science, management, engineering, medicine, physics, biology, hydrology, and computer science. Classical probability distributions have been used to handle data sets of various sizes. However, significant problems arise when real-world data do not fit  any  classical or conventional probability model. Therefore, it is important to improve the flexibility of existing probability models by introducing a combination of two distributions or introducing additional parameters.

 This work presents a new XLindley distribution with a single parameter. Both symmetric and left-skewed data can be used with the proposed model. We generate statistical features such as  mode , mean, variance and  moments functions to accurately represent the utility of the proposed distribution. We compute parameters using  maximum likelihood estimation. An extensive simulation exercise evaluates the fitting performance. We demonstrate the applicability and versatility of the new proposed distribution  using a medical dataset.