Model assisted approaches to complex survey sampling from finite populations using Bayesian Networks

Marco Ballin, Mauro Scanu and Paola Vicard ()

No 54, Departmental Working Papers of Economics - University 'Roma Tre' from Department of Economics - University Roma Tre

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 Monte Carlo 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 probability updating system of the Bayesian networks.

Keywords: Graphical models; probability update; survey design (search for similar items in EconPapers)
Pages: 28
Date: 2005-12
New Economics Papers: this item is included in nep-ecm
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