  Zero-inflated regression models have had wide application recently and have
provenuseful in modeling data with many zeros. Zero-inflated Binomial (ZIB)
regression model is an extension of the ordinary binomial distribution that
takes into account the excess of zeros. In comparing the probit model to the
logistic model, many authors believe that there is little theoretical
justification in choosing one formulation over the other in most circumstances
involving binary responses. The logit model is considered to be computationally
simpler but it is based on a more restrictive assumption of error independence,
although many other generalizations have dealt with that assumption as well. By
contrast, the probit model assumes that random errors have a multivariate
normal distribution. This assumption makes the probit model attractive because
the normal distribution provides a good approximation to many other
distributions. In this paper, we develop a maximum likelihood estimation
procedure for the parameters of a zero-inflated Binomial regression model with
probit link function for both component of the model. We establish the
existency, consistency and asymptotic normality of the proposed estimator.
