Venerdì 29 ottobre 2010, ore 12, Aula 201
Paola VICARD - Dipartimento di Economia, Università degli Studi di Roma Tre
Introduce: M.Giovanna Ranalli, Università di Perugia
Abstract:

Statistical analyses can be particularly complex when are referred
to surveys and databases produced by a National Institute of Statistics.
The complexity is mainly due to: high number of surveys carried out by
the institute, sampling design complexity, high number of variables and
huge sample sizes. In this context it can be useful to analyse and exploit
the dependence structures. Bayesian networks are multivariate statistical
models able to represent and manage complex dependence structures.
The theoretical setting of BNs and of graphical models is the basis for
developing methods for efficiently representing and managing survey systems.
A known (or previously estimated) dependence structure can help:
in estimators computation (with the sampling design either explicitly or
implicitly modelled); when coherence constraints among different surveys
must be fulfilled; in integrating different sample surveys (in terms of
their joint distribution); in updating the estimate of a joint distribution
once new knowledge on a variable marginal distribution occurs (survey weights
poststratification is a special case); in missing data imputation.
Furthermore, since BNs can be extended to embody decision and utility
nodes giving rise to BNs for decisions, they can be used for data collection
monitoring that is carried out on the basis of various factors determining
the final quality of the survey.
The presentation will survey recent results on the application of BNs in the
survey process contexts focusing on categorical variables.