Handling missing values in exploratory multivariate data analysis methods

Julie Josse (1) , François Husson (1)

1 IRMAR - Institut de Recherche Mathématique de Rennes

This paper is a written version of the talk Julie Josse delivered at the 44 Journées de Statistique (Bruxelles, 2012), when being awarded the Marie-Jeanne Laurent-Duhamel prize for her Ph.D. dissertation by the French Statistical Society. It proposes an overview of some results, proposed in Julie Josse and François Husson's papers, as well as new challenges in the field of handling missing values in exploratory multivariate data analysis methods and especially in principal component analysis (PCA). First we describe a regularized iterative PCA algorithm to provide point estimates of the principal axes and components and to overcome the major issue of overfitting. Then, we give insight in the parameters variance using a non parametric multiple imputation procedure. Finally, we discuss the problem of the choice of the number of dimensions and we detail cross-validation approximation criteria. The proposed methodology is implemented in the R package missMDA.

Mots clés: Missing values; PCA; Multiple imputation; MCA; EM algorithm; Regularization; Residual bootstrap; Number of dimensions; Generalized cross-validation

Domaines: Statistiques [math.ST]; Théorie [stat.TH]

Julie Josse, François Husson. Handling missing values in exploratory multivariate data analysis methods. Journal de la Société Française de Statistique, 2012, 153 (2), pp.79-99. ⟨hal-00811888⟩