Research topics

Keywords: missing data, multiple imputation, factorial analysis, multilevel data, clustering
PhD thesis: Multiple imputation using principal component methods: a new methodology to deal with missing values
Manuscript (in french) - PhD defense (in english)

Papers

  1. Audigier, V., Husson, F. & Josse, J. (2016). A principal components method to impute mixed data. Advances in Data Analysis and Classification. pdf - bib - material - R package.
  2. Audigier, V., Husson, F. & Josse, J. (2016). Multiple imputation for continuous variables using a Bayesian principal component analysis. Journal of Statistical Computation and Simulation. pdf - bib - material - R package.
  3. Audigier, V., Husson, F. & Josse, J. (2017). MIMCA: Multiple imputation for categorical variables with multiple correspondence analysis. Statistics and Computing. pdf - bib - data - material - R package
  4. Audigier, V., White, I. ,  Jolani , S.  Debray, T.,  Quartagno, M., Carpenter, J., van Buuren, S. and Resche-Rigon, M. (2018)
    Multiple imputation for multilevel data with continuous and binary variables. Statistical Science. pdf - bib - material - R package
  5. Bar-Hen, A. & Audigier, V. An ensemble learning method for variable selection: application to high dimensional data and missing values. pdf - bib
  6. Audigier, V. & Niang, N. Clustering with missing data: which equivalent for Rubin's rules? pdf - bib

Conferences

  1. Audigier, V., Josse, J. & Husson, F. (2012): Missing values imputation for mixed data based on principal component methods.  COMPSTAT, Cyprus, August 27-31th. slides.
  2. Audigier, V., Josse, J. & Husson, F. (2012): Imputation de données manquantes pour des données mixtes via les méthodes factorielles grâce à missMDA. abstract - slides. Premières rencontres R, Bordeaux, July 2-3th.
  3. Audigier, V., Josse, J. & Husson, F. (2013): Imputation multiple a l'aide des methode d'analyse factorielle. 45eme Journees de Statistique, Toulouse, Mai 27-31th. abstract - slides
  4. Josse, J.,  Husson, F., Audigier, V. (2013). Imputation of mixed data: Random Forests versus PCA. ERCIM, London, December, 14-16th. slides
  5. Audigier, V., Josse, J. & Husson, F. (2014): Multiple imputation with Bayesian PCA. 46eme Journees de Statistique, Rennes, June 2-6th. abstract - slides.
  6. Audigier, V., Josse, J. & Husson, F. (2014): Multiple imputation with MCA. Journées de Statistique de Rennes, Rennes, October 23-24th. slides.
  7. Audigier, V., Josse, J. & Husson, F. (2015): Multiple imputation with MCA. 47eme Journées de Statistique, Lille, June 1-5th. slides.
  8. Audigier, V., Josse, J. & Husson, F. (2015): Multiple imputation for categorical data using MCA. missData2015, Rennes, June 17-19th. slides.
  9. Audigier, V., Josse, J. & Husson, F. (2015): Multiple imputation with MCA. CARMES, Naples, September 21-23th. slides.
  10. Audigier, V., Josse, J. & Husson, F. (2015): Multiple imputation with MCA. Rencontres doctorales Lebesgue, Nantes, October 28-30th. slides.
  11. Audigier, V., White, I. ,  Jolani ,S.  Debray, T.,  Quartagno, M. van Buuren S. & Resche-Rigon, M. (2016): Comparison of multiple imputation methods for systematically and sporadically missing multilevel data. Journées GDR / SFB, Lyon, June, 27-28th. slides
  12. Audigier, V., White, I. ,  Jolani ,S.  Debray, T.,  Quartagno, M. van Buuren S. & Resche-Rigon, M. (2016): Comparison of multiple imputation methods for systematically and sporadically missing multilevel data. ISCB, Birmingham, August, 21-25th. slides
  13. Audigier, V., White, I. ,  Jolani ,S.  Debray, T.,  Quartagno, M. van Buuren S. & Resche-Rigon, M. (2017): Multiple imputation for multilevel data with continuous and binary variables. SMB, Paris, September, 14-15th. poster
  14. Audigier, V., White, I. ,  Jolani ,S.  Debray, T.,  Quartagno, M. van Buuren S. & Resche-Rigon, M. (2018): Multiple imputation for multilevel data with continuous and binary variables. Chimiometrie 2018, Paris, January, 30-31st. slides
  15. Audigier, V., White, I. ,  Jolani ,S.  Debray, T.,  Quartagno, M. van Buuren S. & Resche-Rigon, M. (2018): Multiple imputation for multilevel data with continuous and binary variables. Journée de rencontres scientifiques autour de la statistique pour la biologie et la médecine, Poitiers, February, 1st. slides
  16. Bar-Hen, A. and Audigier, V. (2018): Une méthode d'ensemble pour la sélection de variables : application à la grande dimension et aux données manquantes. 50eme journée de la statistique. Saclay, May 28th - June 1st. slides
  17. Audigier, V, Husson, F., Resche-Rigon, M. & Josse, J. (2019): Imputation multiple pour données mixtes par analyse factorielle. 51eme Journées de Statistique, Nancy, June 3-7th. slides
  18. Faucheux, L., Resche-Rigon, M,  Audigier, V., Curis, E.,  Soumelis, V. and Chevret, S. (2019) : Clustering with missing data: Pooling multiple imputation results with consensus clustering, ISCB, Leuven, July 14th-18th
  19. Audigier, V., Resche-Rigon, M. (2019): micemd: a smart multiple imputation R package for missing multilevel data. UseR!2019, Toulouse, July 9-12th. slides
  20. Audigier, V. , Niang, N. (2021): Cluster analysis after multiple imputation. ASMDA 2021, Athens, June 1-4th. abstract - slides
  21. Audigier, V. , Niang, N., Resche-Rigon, M. (2021): Clustering sur données incomplètes : quel modèle d’imputation choisir ? EPICLIN, Marseille, June 8-11th. slides

Seminars


  1. Josse, J., Husson F., Audigier, V. (2014). Multiple imputation with Bayesian PCA. Séminaire de l'institut de mathématiques de Bordeaux, March 6th. slides
  2. Audigier, V., Josse, J. & Husson, F. (2015): Multiple imputation using principal component methods. SBIM Seminar, Paris, July 15th. slides.
  3. Audigier, V., Josse, J. & Husson, F. (2016): Multiple imputation with principal component methods. Séminaire de l'ISPED, Bordeaux, March, 14th. slides.
  4. Audigier, V., Josse, J. & Husson, F. (2016): Multiple imputation with principal component methods. Midia meeting, London, March, 23th. slides.
  5. Audigier, V., Josse, J. & Husson, F. (2016): Multiple imputation with principal component methods. Séminaire de l'IRMA, Strasbourg, March, 29th. slides.
  6. Audigier, V., White, I. ,  Jolani ,S.  Debray, T.,  Quartagno, M. van Buuren S. & Resche-Rigon, M. (2016): Comparison of multiple imputation methods for systematically and sporadically missing multilevel data. Midia meeting, London, September, 28th. slides.
  7. Audigier, V., White, I. ,  Jolani ,S.  Debray, T.,  Quartagno, M. van Buuren S. & Resche-Rigon, M. (2016): Comparison of multiple imputation methods for systematically and sporadically missing multilevel data. Séminaire de l'équipe INRIA MODAL, Lille, November, 22th. slides
  8. Audigier, V. (2017): Some contributions for handling missing values by multiple imputation. Séminaire de statistique appliquée du CNAM. Paris, September, 29th. slides
  9. Audigier, V., Josse, J. & Husson, F. (2017): Multiple imputation with principal component methods. Data Science Seminar de Telecom Paristech, Paris, November, 16th. slides.
  10. Audigier, V., White, I. ,  Jolani ,S.  Debray, T.,  Quartagno, M. van Buuren S. & Resche-Rigon, M. (2017): Multiple imputation for multilevel data with continuous and binary variables. Séminaire de Statistique et Probabilités Appliquées du LJK, Grenoble, December, 14th. slides
  11. Bar-Hen, A. and Audigier, V. (2018): An ensemble learning method for variable selection: application to high dimensional data and missing values. Midia meeting, London, November, 28th. slides
  12. Bar-Hen, A. and Audigier, V. (2019): An ensemble learning method for variable selection. Sino-French meeting, Paris, October, 12th.
  13. Bar-Hen, A. and Audigier, V. (2019): An ensemble learning method for variable selection: application to high dimensional data and missing values. SBIM Seminar, Paris, November 5th. slides
  14. Audigier, V. , Niang, N., Resche-Rigon, M. (2021): Clustering with missing data: which imputation model for which cluster analysis method? Séminaire MAP5, Paris, June 4th. slides
  15. Audigier, V. , Niang, N., Resche-Rigon, M. (2021): Clustering with missing data: which imputation model for which cluster analysis method? Midia meeting, London, 18th. slides