Bivariate zero-inflated Poisson regression models have recently been used in various medical and biological settings to model excess zeros. However, there has not been any definite approach to deal with the same in the event of missing responses. The model itself is complex and as the responses are paired, missing values can occur in either or both coordinates. We propose a flexible Monte Carlo expectation maximization based approach to handle bivariate zero inflated count data with missing responses. We report the results of a simulation study designed to evaluate the performance of the proposed approach. To illustrate the application of our model and methodology, we consider a bivariate data concerning the demand for health care in Australia.
['Miao Yang', 'Kalyan Das', 'Anandamayee Majumdar']
Analysis of bivariate zero inflated count data with missing responses