Estimation for zero-inflated beta-binomial regression model with missing response data

Discrete data in the form of proportions with overdispersion and zero inflation can arise in toxicology and other similar fields. In regression analysis of such data, another problem that also may arise in practice is that some responses may be missing. In this paper, we develop estimation procedure for the parameters of a zero-inflated overdispersed binomial model in the presence of missing responses under three different missing data mechanisms. A weighted expectation maximization algorithm is used for the maximum likelihood estimation of the parameters involved. Extensive simulations are conducted to study the properties of the estimates in terms of average of estimates, relative bias, variance, mean squared error, and coverage probability of estimates. Simulations show much superior properties of the estimates obtained using the weighted expectation maximization algorithm. Some illustrative examples and a discussion are given.