Time series models for count data have found increased interest in recent days. The existing literature refers to the case of data that have been fully observed. In the present paper, methods for estimating the parameters of the first-order integer-valued autoregressive model in the presence of...

Inference for mixture models based on likelihood estimates suffers from lack of robustness. The presence of a few spurious observations may lead to incorrect decisions. In this paper we consider robust alternatives to the likelihood inference for finite Poisson mixtures based on the minimum...

Finite Poisson mixtures can be used in a variety of real applications to describe count data as they can describe situations where overdispersion relative to the simple Poisson model is present. They also admit a natural interpretation: the entire population is a mixture of k subpopulations each...