Abstract
We propose a generalized class of distributions called the Webull-G Power Series (WGPS) family of distributions and its sub-model Weibull-G logarithmic (WGL) distributions. Structural properties of the WGPS family of distributions and its sub-model WGL distribution including hazard function, moments, conditional moments, order statistics, R´enyi entropy and maximum likelihood estimates are derived. A simulation study to examine the bias, mean square error of the maximum likelihood estimators for each parameter is presented. Finally, real data examples are presented to illustrate the applicability and usefulness of the proposed model.
Highlights
There has been tremendous interest in the generalization or modification of the well-known classical distributions in order to provide better and flexible models for different real life applications
We propose a generalized class of distributions called the Webull-G Power Series (WGPS) family of distributions and its sub-model Weibull-G logarithmic (WGL) distributions
We propose a new class of generalized distributions called the Weibull-G power series (WGPS) family of distributions and its sub-model Weibull-G logarithmic family of distributions
Summary
There has been tremendous interest in the generalization or modification of the well-known classical distributions in order to provide better and flexible models for different real life applications. We propose a new class of generalized distributions called the Weibull-G power series (WGPS) family of distributions and its sub-model Weibull-G logarithmic family of distributions. We compound the Weibull-G family and power series distributions, and introduce a new class of distributions and its sub-model called the Weibull-G logarithmic (WGL) family of distributions. This content of this paper is organized as follows. The generalized family of distributions called the Weibull-G power series (WGPS) family of distributions and some of its properties including expansion of the density, hazard function, quantile function and sub-models, moments, conditional moments and maximum likelihood estimation of model parameters are derived.
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