F. Husson and J. Josse, missMDA : Handling missing values with/in multivariate data analysis (principal component methods), 2010.

F. Husson, J. Josse, S. Le, and J. Mazet, FactoMineR : Multivariate Exploratory Data Analysis and Data Mining with R. R package version 1, p.16, 2011.

A. Ilin and T. Raiko, Practical approaches to principal component analysis in the presence of missing values, Journal of Machine Learning Research, vol.11, 1957.

J. Josse, M. Chavent, B. Liquet, and F. Husson, Handling Missing Values with Regularized Iterative Multiple Correspondence Analysis, Journal of Classification, vol.33, issue.1, pp.91-116, 2012.
DOI : 10.1007/s00357-012-9097-0

URL : https://hal.archives-ouvertes.fr/hal-00647378

J. Josse, J. Pagès, and F. Husson, Gestion des données manquantes en analyse en composantes principales, Journal de la Société Française de Statistique, vol.150, pp.28-51, 2009.

H. A. Kiers, Weighted least squares fitting using ordinary least squares algorithms, Psychometrika, vol.18, issue.2, pp.251-266, 1997.
DOI : 10.1007/BF02295279

S. Lê, J. Josse, and F. Husson, Factominer : An r package for multivariate analysis, Journal of Statistical Software, vol.3, issue.251, pp.1-18, 2008.

D. Stekhoven and P. Buhlmann, Missforest -nonparametric missing value imputation for mixed-type data, Bioinformatics, vol.28, pp.113-118, 2011.