1,720,990 research outputs found
"Graphical chain models for the analysis of complex genetic diseases:an application to hypertension",
No full textGraphical model using copulas for measurement error modeling.
No full textIn 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
Statistical Micro Matching Using Bayesian Networks
No full textThe goal of statistical matching, at a micro level, is the construction of a
synthetic data source where all the variables of interest are available. In this paper
we propose the use of Bayesian Networks to deal with the statistical matching for
multivariate categorical variables in the micro approach. Its performance is evaluated
by an application to a real data set
Measurement error modelling using object-oriented Bayesian networks
No full textVariables are rarely, if ever, measured without error. In this paper we propose to use the Object-Oriented Bayesian Networks architecture to model measurement errors. A mixed measurement error model is introduced to model the respondent error. Then an Object-Oriented Bayesian network, implementing the model
above, is developed to represent how the actually observed values are generated
from the original ones. Furthermore, potentialities and possible extensions of such
an approach are discussed
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