New Odd Generalized Exponentiated Exponential-G Family of Distributions

Abstract

In the last decades, many researchers have developed new methods for generating families of distributions. These generators are obtained by adding one or more extra shape parameter(s) to the baseline distribution to achieve more flexibility for modelling real lifetime data sets. The additional parameter(s) has been proven useful by obtaining tail properties and improving the analysis from the goodness-of-fit for the families of distributions under study. In this paper, we proposed a new family of distributions called the New Odd Generalized Exponentiated Exponential-G Family of Distributions. The statistical properties are derived, and the maximum likelihood estimation technique is described for the proposed new family of distributions. The new models' performance is illustrated by numerical analysis using a real-life dataset, and the dataset shows that the new models offer a better fit compared to other competing models.