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Bayesian networks for imputation

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Bayesian networks are particularly useful for dealing with high dimensional statistical problems. They allow a reduction in the complexity of the phenomenon under study by representing joint relationships between a set of variables through conditional relationships between subsets of these variables. Following Thibaudeau and Winkler we use Bayesian networks for imputing missing values. This method is introduced to deal with the problem of the consistency of imputed values: preservation of statistical relationships between variables (statistical consistency) and preservation of logical constraints in data (logical consistency). We perform some experiments on a subset of anonymous individual records from the 1991 UK population census.

Keywords: EM algorithm; Graphical structure; Hot deck; Item non-response; Multivariate analysis

Document Type: Research Article


Publication date: May 1, 2004

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