Model assisted approaches to complex survey sampling from finite populations using Bayesian Networks
AbstractA 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.
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Bibliographic InfoPaper provided by Department of Economics - University Roma Tre in its series Departmental Working Papers of Economics - University 'Roma Tre' with number 0054.
Date of creation: Dec 2005
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Graphical models; probability update; survey design;
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