Ricerc@Sapienza

In wildlife animal populations, as well as in human populations, appropriate statistical models should be conceived for inferring the unknown size of a finite population when there is no available or reliable information on the complete enumeration and identification of each units of the target population. Although this is the rule in the wildlife management, it is also often the case in the social sciences and medical sciences, where there are multiple sources or registers for a target population, but none of them is fully exhaustive.

A lot of research have been developed on rigorously approaching the problem of inferring the complete enumeration by means of multiple recording systems. Mutuating from the wildlife management, this class of statistical models is often referred to as capture-recapture models. Individual data derives from the results of consecutive capture stages (sources, registers or recording systems) where the unit captured for the first time is marked so that once it is observed again in the next stage can be identified and is considered as a recapture.

In this research we propose to approach the study of the statistical models related to counting distributions as well as multiple binary outcomes from a Bayesian inferential perspective. We propose to extend the available models and tools in the presence of several sources of heterogeneity as well as external auxiliary information on the characteristics of the population units as well as on the recording system. The advantages of the Bayesian approach have been only recently specifically focussed in terms of 1) flexibility of the model framework due to its natural ability to incorporate latent unobserved features and 2) inferential improvements on the precision of the resulting estimators. We also propose to provide suitable Bayesian tools for planning the configuration of the capture-recapture experiments.