In this paper recent results about the application of Bayesian networks to official statistics are presented. Bayesian networks are multivariate statistical models able to represent and manage complex dependence structures. Here they are proposed as a useful and unique framework by which it is possible to deal with many problems typical of survey data analysis. In particular here we focus on categorical variables and show how to derive classes of contingency table estimators in case of stratified sampling designs. Having this technology it becomes possible to perform poststratification, integration and missing data imputation. Furthermore we briefly discuss how to use Bayesian networks for decision as a support system to monitor and manage the data production process.
|Titolo:||Application of Bayesian networks in Official Statistics|
|Data di pubblicazione:||2012|
|Citazione:||VICARD P, & SCANU M (2012). Application of Bayesian networks in Official Statistics. In C. Di Ciaccio (a cura di), Advanced Statistical methods for the analysis of large data-sets (pp. 113-125). Berlin Heidelberg : Springer.|
|Appare nelle tipologie:||2.1 Contributo in volume (Capitolo o Saggio)|