Handling Missing Values with Regularized Iterative Multiple Correspondence Analysis |
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Authors: | Julie Josse Marie Chavent Benot Liquet Fran?ois Husson |
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Institution: | 1. Agrocampus Rennes, 65 rue de St-Brieuc, 35042, Rennes, France 2. Universit?? V. Segalen Bordeaux 2, Bordeaux, France 3. Equipe Biostatistique de l??U897 INSERM ISPED, Bordeaux, France
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Abstract: | A common approach to deal with missing values in multivariate exploratory data analysis consists in minimizing the loss function
over all non-missing elements, which can be achieved by EM-type algorithms where an iterative imputation of the missing values
is performed during the estimation of the axes and components. This paper proposes such an algorithm, named iterative multiple
correspondence analysis, to handle missing values in multiple correspondence analysis (MCA). The algorithm, based on an iterative
PCA algorithm, is described and its properties are studied. We point out the overfitting problem and propose a regularized
version of the algorithm to overcome this major issue. Finally, performances of the regularized iterative MCA algorithm (implemented in the R-package named missMDA) are assessed from both simulations and a real dataset. Results are
promising with respect to other methods such as the missing-data passive modified margin method, an adaptation of the missing passive method used in Gifi’s Homogeneity analysis framework. |
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