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We propose a new method to impute missing values in mixed datasets. It isbased on a principal components method, the factorial analysis for mixed data,which balances the influence of all the variables that are continuous andcategorical in the construction of the dimensions of variability. Because theimputation uses the principal axes and components, the prediction of themissing values are based on the similarity between individuals and on therelationships between variables. The quality of the imputation is assessedthrough a simulation study and real datasets. The method is compared to arecent method (Stekhoven and B\"uhlmann, 2011) based on random forests andshows better performances especially for the imputation of categoricalvariables and when there are highly linear relationships between continuousvariables.

A principal components method to impute missing values for mixed data