Moment-based Bayesian Poisson Mixtures for inferring unobserved units
Methodology
2018-06-19 v1
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.
Keywords
Cite
@article{arxiv.1806.06489,
title = {Moment-based Bayesian Poisson Mixtures for inferring unobserved units},
author = {Danilo Alunni Fegatelli and Luca Tardella},
journal= {arXiv preprint arXiv:1806.06489},
year = {2018}
}