Dealing with missing data through mixture models

Vandewalle, Vincent; Biernacki, Christophe

Document type :

Communication dans un congrès avec actes

Title :

Dealing with missing data through mixture models

Author(s) :

Vandewalle, Vincent [Auteur]

MOdel for Data Analysis and Learning [MODAL]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
Université de Lille, Droit et Santé
Biernacki, Christophe [Auteur]
Laboratoire Paul Painlevé - UMR 8524 [LPP]
MOdel for Data Analysis and Learning [MODAL]
Université de Lille, Sciences et Technologies

Conference title :

ICB Seminars 2017 - 154th Seminar on ”Statistics and clinical practice”

City :

Varsovie

Country :

Pologne

Start date of the conference :

2017-05-11

HAL domain(s) :

Statistiques [stat]

English abstract : [en]

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 ...
Show more >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.Show less >

Language :

Anglais

Peer reviewed article :

Oui

Audience :

Internationale

Popular science :

Non

Collections :

Laboratoire Paul Painlevé - UMR 8524

Source :

Harvested from HAL

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