In many practical fields, including engineering, medicine, and finance, among others, right or left skewness, bi-modality, or multi-modality are characteristics of data sets that can be modelled using statistical distributions. Because of their straightforward shapes and identifiability characteristics, well-known distributions, such as normal, Weibull, gamma, and Lindley, are frequently utilised. However, during the past ten years, much research has concentrated on the more flexible and complicated Generalized or simply G families of continuous distributions to improve their modelling capabilities by including one or more shape parameters. This book attempts to compile some new results using such distributions that are valuable in theory and application. It is motivated by adding one or more parameters to a distribution function makes it more versatile and more flexible in analysing data. The book also examines the characteristics of a few novel G families and how they might be used for statistical inference. Results are collected that could be added to those already available. The primary goal of our book is to compile recent advances made by diverse authors in the field of G families of their contributions to these new distributions into an edited book. This book will help present and future scholars studying the G family of probability distributions to generate additional new univariate continuous G families of probability distributions; derive valuable mathematical properties, including entropies, order statistics, quantile spread ordering, ordinary and incomplete moments, moments generating functions, residual life and reversed residual life functions, among others and apply the Farlie Gumbel Morgenstern copula, the modified Farlie Gumbel Morgenstern copula, the Clayton copula, the Renyi entropy copula and the Ali-Mikhail-Haq copula for deriving bivariate and multivariate expansions of the new and existing G families. This book stands out because it includes a lot of new G families, each with its characteristics and applications to diverse real datasets and simulation studies utilising various estimation methods. In the field of statistical modelling, the book deals with analysing and studying actual data that differ in nature and shap
