A principal component method to impute missing values for mixed data

Abstract

We propose a new method to impute missing values in mixed data sets. It is based on a principal component method, the factorial analysis for mixed data, which balances the influence of all the variables that are continuous and categorical in the construction of the principal components. Because the imputation uses the principal axes and components, the prediction of the missing values is based on the similarity between individuals and on the relationships between variables. The properties of the method are illustrated via simulations and the quality of the imputation is assessed using real data sets. The method is compared to a recent method (Stekhoven and Buhlmann Bioinformatics 28:113–118, 2011) based on random forest and shows better performance especially for the imputation of categorical variables and situations with highly linear relationships between continuous variables.

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Correspondence to François Husson.

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Audigier, V., Husson, F. & Josse, J. A principal component method to impute missing values for mixed data. Adv Data Anal Classif 10, 5–26 (2016). https://doi.org/10.1007/s11634-014-0195-1

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Keywords

  • Missing values
  • Mixed data
  • Imputation
  • Principal component method
  • Factorial analysis of mixed data

Mathematics Subject Classification

  • 62H25