"Modelling Zero-inflated Bivariate Count Responses using Marginal-Conditional Approach with application to Traffic Accidents and Fatalities Data"

Abstract

Zero-inflated bivariate count responses frequently occur in medical, environmental, ecological, social and transportation studies. These bivariate count responses recorded from a group of independent subjects or from a specific time or place may suffer the presence of excessive zeros, so constitute complexity while modelling. Although the responses under the umbrella of zero-inflated clustered and longitudinal count setup have been studied extensively in the past two decades, the literature is rather limited for analyzing zero-heavy bivariate count data. In this paper, we propose a marginal-conditional approach based bivariate zero-inflated Poisson-Poisson regression model to overcome the complexity imposed due to excessive zeros in bivariate count responses.