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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, Université de Lille, Sciences et Technologies, Inria Lille - Nord Europe, METRICS - Evaluation des technologies de santé et des pratiques médicales - ULR 2694, Polytech Lille - École polytechnique universitaire de Lille
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.
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Submitted on : Tuesday, December 19, 2017 - 4:21:17 PM
Last modification on : Wednesday, March 23, 2022 - 3:51:10 PM


  • HAL Id : hal-01667614, version 1



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⟩



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