The exponentiated generalized family of continuous distributions

Abstract

Statistical and applied researchers have shown great interest in building new extended probability models that generalize well-known distributions and are more flexible for data modeling in many fields of applications. Probably, one of the most popular ways to extend well-known models is to consider distribution generators. Aclass of univariate distributions called the exponentiated generalized (EG for short) class was recently proposed in the literature. We believe that the EG class of distributions can be widely used to generalize continuous distributions. For this reason, the present doctoral thesis presents some extended models using the EG class. For each model presented in the chapters that follow, we provide a complete mathematical treatment, simulation studies and applications to real data that illustrate the usefulness of the model sunder study.