Dealing with missing data through mixture models
Type de document :
Communication dans un congrès avec actes
URL permanente :
Titre :
Dealing with missing data through mixture models
Auteur(s) :
Vandewalle, Vincent [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
Biernacki, Christophe [Auteur]
Evaluation des technologies de santé et des pratiques médicales - ULR 2694 [METRICS]
METRICS : Evaluation des technologies de santé et des pratiques médicales - ULR 2694
Biernacki, Christophe [Auteur]
Titre de la manifestation scientifique :
ICB Seminars 2017 - 154th Seminar on ”Statistics and clinical practice”
Ville :
Varsovie
Pays :
Pologne
Date de début de la manifestation scientifique :
2017-05-11
Date de publication :
2017-05-11
Discipline(s) HAL :
Statistiques [stat]
Résumé en anglais : [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 ...
Lire la suite >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.Lire moins >
Lire la suite >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.Lire moins >
Langue :
Anglais
Audience :
Internationale
Vulgarisation :
Non
Établissement(s) :
CHU Lille
Université de Lille
Université de Lille
Date de dépôt :
2020-06-08T14:10:23Z
2020-06-09T08:25:04Z
2020-06-09T08:25:04Z
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