In this paper the problem of detection and correction of errors in the Banca d’Italia Survey on Household Income and Wealth is considered. A novel error correction procedure is proposed. It is based on non parametric Bayesian networks that are graphical models expressing dependence structure via bivariate copulas associated to the edges of the graph. The network structure and the misreport probability are estimated using a validation sample. The correction procedure is applied to bond amounts.
Marella, D., Vicard, P., Vitale, V. (2017). NON PARAMETRIC BAYESIAN NETWORKS FOR MEASUREMENT ERROR DETECTION. In Cladag 2017-Book of short papers. Mantova : Universitas Studiorum S.r.l. Casa Editrice.
NON PARAMETRIC BAYESIAN NETWORKS FOR MEASUREMENT ERROR DETECTION
MARELLA, Daniela;VICARD, Paola;VITALE, VINCENZINA
2017-01-01
Abstract
In this paper the problem of detection and correction of errors in the Banca d’Italia Survey on Household Income and Wealth is considered. A novel error correction procedure is proposed. It is based on non parametric Bayesian networks that are graphical models expressing dependence structure via bivariate copulas associated to the edges of the graph. The network structure and the misreport probability are estimated using a validation sample. The correction procedure is applied to bond amounts.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
