BIVARIATE EXTENDED POISSON-LINDLEY DISTRIBUTION BASED ON SARMANOV-LEE FAMILY

In this article, we propose a new discrete bivariate distribution, called the Bivariate Extended Poisson-Lindley (BEPL) distribution, suitable for modeling overdispersed and correlated count data. The distribution is constructed within the Sarmanov-Lee family by combining two Extended Poisson-Lindley distributions via a multiplicative factor. We analyze its theoretical properties (moment-generating function, various moments) and estimate its parameters using the maximum likelihood method. The model's performance is evaluated and compared to that of existing discrete bivariate models on two real-world datasets. The proposed distribution exhibits great flexibility in capturing different types of dependencies (positive, zero, or negative correlations), providing an effective tool for modeling correlated count data.