Semiparametric inference based on a class of zero-altered distributions

In modeling count data collected from manufacturing processes, economic series, disease outbreaks and ecological surveys, there are usually a relatively large or small number of zeros compared to positive counts. Such low or high frequencies of zero counts often require the use of underdispersed or overdispersed probability models for the underlying data generating mechanism. The commonly used models such as generalized or zero-inflated Poisson distributions are parametric and can usually account for only the overdispersion, but such distributions are often found to be inadequate in modeling underdispersion because of the need for awkward parameter or support restrictions. This article introduces a flexible class of semiparametric zero-altered models which account for both underdispersion and overdispersion and includes other familiar models such as those mentioned above as special cases. Consistency and asymptotic normality of the estimator of the dispersion parameter are derived under general conditions. Numerical support for the performance of the proposed method of inference is presented for the case of common discrete distributions.