A class of estimators based on the dependency structure of a multivariate variable of interest and the survey design is defined. The dependency structure is the one described by the Bayesian networks. This class allows ratio type estimators as a subclass identified by a particular dependency structure. It will be shown by a MonteCarlo simulation how the adoption of the estimator corresponding to the population structure is more efficient than the others. It will also be underlined how this class adapts to the problem of integration of information from two surveys through the probability updating system of the Bayesian networks.
Ballin, M., Scanu, M., Vicard, P. (2005). Model Assisted Approaches to Complex Survey Sampling from Finite Populations Using Bayesian Networks: a Tool for Integration of different Sources. In Statistics Canada International Symposium Series - Proceedings: Methodological Challenges for Future Information Needs. Statistics Canada.
Model Assisted Approaches to Complex Survey Sampling from Finite Populations Using Bayesian Networks: a Tool for Integration of different Sources
VICARD, Paola
2005-01-01
Abstract
A class of estimators based on the dependency structure of a multivariate variable of interest and the survey design is defined. The dependency structure is the one described by the Bayesian networks. This class allows ratio type estimators as a subclass identified by a particular dependency structure. It will be shown by a MonteCarlo simulation how the adoption of the estimator corresponding to the population structure is more efficient than the others. It will also be underlined how this class adapts to the problem of integration of information from two surveys through the probability updating system of the Bayesian networks.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
