Research topics

Keywords: missing data, multiple imputation, factorial analysis, multilevel data, clustering

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. Journal of Statistical Computation and Simulation. (2022) pdf - bib
  6. Audigier, V. & Niang, N. (2022) Clustering with missing data: which equivalent for Rubin's rules? Advances in Data Analysis and Classification pdf - bib - R package
  7. Munoz, J., Egger, M., Efthimiou, O., Audigier, V., de Jong, V. M. T. and Debray, T.. P. A. (2023) Multiple imputation of incomplete multilevel data using Heckman selection models. Statistics in Medicine pdf - bib - R package To appear

Preprints

  1. Audigier, V. & Niang, N., Resche-Rigon, M. Clustering with missing data: which imputation model for which cluster analysis method? pdf - bib - R package
  2. Mouhou, E., Audigier, V., Noirel, J. A simple Bayesian model to estimate proportions and ratios from count data with a hierarchical error structure with an application to droplet digital PCR experiments data: which imputation model for which cluster analysis method? pdf - bib

Book chapters

  1. Audigier, V. , Gestion des données manquantes par imputation multiple (2022). Gégout-Petit, A.; Maumy, M.; Saporta, G.; Thomas-Agnan, C.. Données manquantes, Editions TECHNIP, ISBN 9782710811954.
  2. Audigier, V. . Imputation multiple en grande dimension par analyse factorielle (2022). Gégout-Petit, A.; Maumy, M.; Saporta, G.; Thomas-Agnan, C.. Données manquantes, Editions TECHNIP, ISBN 9782710811954.

PhD Thesis

Audigier, V. (2015) Multiple imputation using principal component methods: a new methodology to deal with missing values Manuscript (in french) - PhD defense (in english)

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.
  22. Hassini, H., Niang, N., Audigier, V. (2021): SOM-based clusterwise regression. Data Science, Statistics and Visualisation, July 7-9th.
  23. Audigier, V., Resche-Rigon, M., Bonneville, E. (2022) Données manquantes et analyse de survie, EPICLIN 2022, Paris, 18th-20th May
  24. Audigier, V. , Niang, N., Resche-Rigon, M. (2022): Clustering with missing data: which imputation model for which cluster analysis method? 17th conference of the International Federation of Classification Societies, Porto, 19th-23th July slides
  25. Audigier, V., Sadou Zouleya, F. (2023): Clustering sur données incomplètes : méthodes directes ou imputation multiple ? 54emes Journées de Statistique, Bruxelles, July 3-7th abstract - slide
  26. Audigier, V., Niang N. (2023): Multiple imputation for clustering on incomplete data. CLADAG, Salerno, September 11-13th. abstract - slides
  27. Audigier, V. (2023): Handling missing data in clustering using multiple imputation. ERCIM, Berlin, December, 16-18th

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
  16. Audigier, V., Niang N. (2023): Multiple imputation for clustering on incomplete data, Machine Learning Worshop, Centre Borelli, September 27th. slides
  17. Audigier, V., Niang N. (2023): Multiple imputation for clustering on incomplete data, Séminaire du laboratoire ERIC, Lyon, October 9th. slides
  18. Audigier, V., Niang N. (2023): Multiple imputation for clustering on incomplete data, Laboratoire de Mathématiques de Bretagne Atlantique, October 20th. slides