Dealing with missing data through mixture models
Document type :
Communication dans un congrès avec actes
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Title :
Dealing with missing data through mixture models
Author(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]
Conference title :
ICB Seminars 2017 - 154th Seminar on ”Statistics and clinical practice”
City :
Varsovie
Country :
Pologne
Start date of the conference :
2017-05-11
Publication date :
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 >
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
Audience :
Internationale
Popular science :
Non
Administrative institution(s) :
CHU Lille
Université de Lille
Université de Lille
Submission date :
2020-06-08T14:10:23Z
2020-06-09T08:25:04Z
2020-06-09T08:25:04Z
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