References on missing values

How to cite the package missMDA?

  • Josse J. & Husson F. (2016). missMDA: a package for handling missing values in multivariate data analysis. Journal of Statistical Software, 70(1), 1-31.

Vignette:

  • Multiple imputation with principal component methods: a user guide written by V. Audigier [pdf]

Papers:

  • Audigier V., Husson F. & Josse J. (2017). MIMCA: multiple imputation for categorical variables with multiple correspondence analysis. Statistics and Computing, 27(2):501-518.
  • Josse J. & Husson F. (2016). missMDA: a package for handling missing values in multivariate data analysis. Journal of Statistical Software, 70(1), 1-31.
  • Audigier V, Husson F & Josse, J. (2016). Multiple imputation for continuous variables using a bayesian principal component analysis. Journal of Statistical Computation and Simulation, 86(11):2140-2156.
  • Audigier V, Husson F & Josse J. (2016). A principal component method to impute missing values for mixed data. Advances in Data Analysis and Classification, 10(1):5-26.
  • Josse J & Husson F. (2013). Handling missing values in exploratory multivariate data analysis methods. Journal de la SFDS. 153 (2), pp.  79-99.
  • Josse J, Chavent M., Liquet B. & Husson F.(2012). Handling missing values with Regularized Iterative Multiple Correspondence Analysis. Journal of classification. 29 (1), pp.91-116.
  • Josse J & Husson F. (2011). Selecting the number of components in PCA using cross-validation approximations.Computational Statististics and Data Analysis. 56 (6), pp. 1869-1879.
  • Josse J, Husson H. & Pagès J. (2011). Multiple imputation in PCA.  Advances in data analysis and classification. 5 (3), pp. 231-246.

Tutorials:

Tutorial done during the useR! 2015 conference (Aalborg, DK)

The tutorial Handling missing values with a special focus on the use of principal components methods done in Aalborg during the useR! 2015 conference.

The following packages are used: FactoMineR, missMDA, VIM, Amelia, norm, mice.

The data sets and the lines of code are available to redo the analyses done in the tutorial.

Tutorials also done:
  • useR!2016, Stanford
  • Gdr Ecology stat, Lyon 2016
  • INRIA, saclay Data Science Center, 2016

Other conferences:

  • Missing values imputation for mixed data based on principal component methods.  COMPSTAT, Cyprus, 27-31th 2012. slides.
  • Imputation de données manquantes pour des données mixtes via les méthodes factorielles grâce à missMDA. Premières rencontres R, Bordeaux. July 2-3th 2012. abstract - slides.
  • missMDA : a package to handle missing values in and with multivariate exploratory data analysis methods. useR ! 2011, Warwick, England, August 15-20th. (abstract, slides).