In this paper the use of non-parametric Bayesian belief networks for modeling measurement error in Italian Survey on Household Income and Wealth 2008 is investigated. Non-parametric Bayesian belief networks are graphical models expressing the dependence structure between the marginals through the use of bivariate copulas associated to the arcs of the graph. Thanks to their directed structure, non-parametric Bayesian belief networks can be easily used for measurement error correction.

Marella D., & Vicard P. (2015). Graphical model using copulas for measurement error modeling.. In Cladag 2015. 10th Meeting of the Classification and Data Analysis Group. Book of Abstracts. (pp.221-224). Cagliari : CUEC Editrice.

Graphical model using copulas for measurement error modeling.

MARELLA, Daniela;VICARD, Paola
2015

Abstract

In this paper the use of non-parametric Bayesian belief networks for modeling measurement error in Italian Survey on Household Income and Wealth 2008 is investigated. Non-parametric Bayesian belief networks are graphical models expressing the dependence structure between the marginals through the use of bivariate copulas associated to the arcs of the graph. Thanks to their directed structure, non-parametric Bayesian belief networks can be easily used for measurement error correction.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11590/308131
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