Dealing with missing data through mixture models

Vincent Vandewalle 1, 2, 3 Christophe Biernacki 4, 1, 5
1 MODAL - MOdel for Data Analysis and Learning
LPP - Laboratoire Paul Painlevé - UMR 8524, Inria Lille - Nord Europe, CERIM - Santé publique : épidémiologie et qualité des soins-EA 2694, Polytech Lille, Université de Lille 1, IUT’A
Abstract : Many data sets have missing values, however the majority of statistical methods need a complete dataset to work. Thus, practitioners often use imputation or multiple imputations to complete the data as a pre-processing step. In this talk it will be shown how mixture models can be used to naturally deal with missing data in an integrated way depending on the purpose. Especially, it will be shown how they can be used to classify the data or derive estimates for the distances. Results on real data will be shown.
Type de document :
Communication dans un congrès
ICB Seminars 2017 - 154th Seminar on ”Statistics and clinical practice”, May 2017, Varsovie, Poland. pp.1-3
Liste complète des métadonnées

https://hal.inria.fr/hal-01667614
Contributeur : Vincent Vandewalle <>
Soumis le : mardi 19 décembre 2017 - 16:21:17
Dernière modification le : mercredi 25 avril 2018 - 14:23:16

Identifiants

  • HAL Id : hal-01667614, version 1

Collections

Citation

Vincent Vandewalle, Christophe Biernacki. Dealing with missing data through mixture models. ICB Seminars 2017 - 154th Seminar on ”Statistics and clinical practice”, May 2017, Varsovie, Poland. pp.1-3. 〈hal-01667614〉

Partager

Métriques

Consultations de la notice

56

Téléchargements de fichiers

28