Moment-based Bayesian Poisson Mixtures for inferring unobserved units

Abstract

We exploit a suitable moment-based characterization of the mixture of Poisson distribution for developing Bayesian inference for the unknown size of a finite population whose units are subject to multiple occurrences during an enumeration sampling stage. This is a particularly challenging setting for which many other attempts have been made for inferring the unknown characteristics of the population. Here we put particular emphasis on the construction of a default prior elicitation of the characteristics of the mixing distribution. We assess the comparative performance of our approach in real data applications and in a simulation study.