%0 Journal Article
%T IMPUTACI¨®N M¨²LTIPLE EN VARIABLES CATEG¨®RICAS USANDO DATA AUGMENTATION Y ¨¢RBOLES DE CLASIFICACI¨®N
%A Jorge Bacallao Guerra* y Jorge Bacallao Gallestey**
%J Revista Investigaci¨®n Operacional
%D 2010
%I Universidad de La Habana
%X It is a modication of common multiple imputation algorithms which combines classification trees (CT) and data augmentation for categorical data. We describe the rationale of the method and compare it, on theoretical and practical grounds, with two of the most frequently used methods. We use a fictitious base and an ¡°ad hoc" R-based software.
%K Multiple Imputation
%K categorical data
%K Data Augmentation
%K classification trees.
%U http://rev-inv-ope.univ-paris1.fr/files/31210/31210-04r.pdf