Measurement error is the difference between the value provided by the respondent and the true (but unknown) value. It is sometimes deﬁned as observation error, since it is related to the observation of the variable at the data collection stage. The problem of measurement error in ﬁnancial assets is studied. The measurement error is modeled by means of non parametric Bayesian belief networks, that are graphical models expressing the dependence structure through bivariate copulas associated to the edges of the graph without introducing any distributional assumption. A new error correction procedure based on non parametric Bayesian belief networks is proposed. Measurement error modeling and microdata correction are illustrated by means of an application to the Banca d’Italia Survey on Household Income and Wealth 2008. The measurement model and its parameters have been estimated via a validation sample. The sensitivity of the conditional distribution of the true value given the observed one to different evidence conﬁgurations is analysed.
|Titolo:||Modeling measurement error via nonparametric Bayesian belief nets|
|Data di pubblicazione:||2015|
|Citazione:||Marella D., & Vicard P. (2015). Modeling measurement error via nonparametric Bayesian belief nets. In 8th International Conference of the ERCIM (European Research Consortium for Informatics and Mathematics) Working Group on Computational and Methodological Statistics (ERCIM 2015)..|
|Appare nelle tipologie:||4.2 Abstract in Atti di convegno|