Ricerc@Sapienza

The quantity of data available to data-holders institutions has been rapidly increasing in recent years. A notable example is the sheer number of administrative sources with individual-level information that become available to national statistical institutes. This new asset of data required a shift of focus to new specific methodologies for statistical analysis in official statistics. A family of models has received renewed attention in this setting, namely the latent class models.
These models are used in various stages of the statistical production with different goals but, in general, they share a common perspective: since these kinds of data are not directly collected for statistical purposes, the information required by the statistician is not perfectly aligned with the information available. As a consequence, we exploit the information redundancy coming from the integration of multiple sources targeting the same units/variables. In this approach, the latent classes are used to model the desired classification of the units.
Examples of applications include every phase of the statistical treatment: from handling record-linkage errors, to editing and imputation, to estimation of the size of a target population.
Latent class models belongs to the wider class of finite mixture models and thus are particularly appreciated for the easiness of use and flexibility, and generated countless extensions in literature. In this project we propose to define and compare different Bayesian approaches to this class, applied to the listed practical context, and to develop a Bayesian procedure to facilitate model selection within the class.