Bivariate Exponentaited Generalized Weibull-Gompertz Distribution

TL;DR

This paper introduces a new bivariate distribution of Marshall-Olkin type, exploring its properties and demonstrating its superior fit to real data compared to existing distributions.

Contribution

The paper presents the first bivariate exponentiated generalized Weibull-Gompertz distribution with detailed properties and estimation methods, expanding the modeling toolkit.

Findings

The distribution accurately models real data, outperforming other well-known distributions.

Properties such as moments, hazard functions, and maximum likelihood estimates are thoroughly derived.

The model provides a better fit for complex bivariate data sets.

Abstract

In this paper, we introduce a bivariate exponentaited generalized Weibull-Gompertz distribution. The model introduced here is of Marshall-Olkin type. Several properties are studied such as bivariate probability density function and it is marginal, moments, maximum likelihood estimation, joint reversed (hazard) function and joint mean waiting time and it is marginal. A real data set is analyzed and it is observed that the present distribution can provide a better fit than some other very well-known distributions.