On Bivariate Generalized Exponential-Power Series Class of Distributions

TL;DR

This paper introduces a new bivariate distribution class by combining generalized exponential and power-series distributions, deriving properties, estimating parameters via EM algorithm, and demonstrating practical application.

Contribution

It presents a novel bivariate distribution class with multiple sub-models, expanding the modeling options for correlated data.

Findings

Derived properties of the new distribution class

Successfully estimated parameters using EM algorithm

Applied the model to real data demonstrating usefulness

Abstract

In this paper, we introduce a new class of bivariate distributions by compounding the bivariate generalized exponential and power-series distributions. This new class contains some new sub-models such as the bivariate generalized exponential distribution, the bivariate generalized exponential-poisson, -logarithmic, -binomial and -negative binomial distributions. We derive different properties of the new class of distributions. The EM algorithm is used to determine the maximum likelihood estimates of the parameters. We illustrate the usefulness of the new distributions by means of an application to a real data set.