Abstract
Abstract
In this article, we develop a general framework for constructing probability distributions arising from κ-deformed quantile functions and investigate the impact of the deformation parameter κ on the distributional and tail characteristics of well-known probability models. Motivated by the observation that κ-deformation can substantially modify the tail behaviour of a baseline distribution, we derive several κ-deformed quantile functions from selected classical distributions. The practical usefulness of the proposed class is illustrated through applications to real-life datasets, where parameter estimation and goodness-of-fit analyses are performed and the fitted models are compared with their classical counterparts.