Analysis of zero-adjusted count data

In this paper a zero adjusted discrete model is developed. Such a situation arises when the proportion of zeros in the data is higher (lower) than that predicted by the original model. The effect of such an adjustment is studied. The failure rates and the survival functions of the adjusted and the non-adjusted models are compared. The relative error incurred by ignoring the adjustment is studied and it is shown that the relative error is a decreasing function of the count. An adjusted generalized Poisson distribution is studied and the three parameters of this model are estimated by the maximum likelihood method. Finally, two examples are presented. In both the examples, it is shown that the zero adjusted generlized Poisson distribution fits very well and the estimates of the parameters are obtained by simple and straight-forward methods.