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A principal components method to impute missing values for mixed data

Abstract : We propose a new method to impute missing values in mixed datasets. It is based on a principal components 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 dimensions of variability. Because the imputation uses the principal axes and components, the prediction of the missing values are based on the similarity between individuals and on the relationships between variables. The quality of the imputation is assessed through a simulation study and real datasets. The method is compared to a recent method (Stekhoven and Bühlmann, 2011) based on random forests and shows better performances especially for the imputation of categorical variables and when there are highly linear relationships between continuous variables.
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https://hal.archives-ouvertes.fr/hal-00832935
Contributor : Marie-Annick Guillemer <>
Submitted on : Tuesday, June 11, 2013 - 4:08:17 PM
Last modification on : Friday, July 10, 2020 - 4:03:25 PM

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Vincent Audigier, François Husson, Julie Josse. A principal components method to impute missing values for mixed data. Advances in Data Analysis and Classification, Springer Verlag, 2016, 10 (1), pp.5-26. ⟨10.1007/s11634-014-0195-1⟩. ⟨hal-00832935⟩

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