Model-based clustering with missing not ...
Document type :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès sans actes
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Title :
Model-based clustering with missing not at random data. Missing mechanism
Author(s) :
Laporte, Fabien [Auteur]
Biernacki, Christophe [Auteur]
Celeux, Gilles [Auteur]
Josse, Julie [Auteur]
Biernacki, Christophe [Auteur]

Celeux, Gilles [Auteur]
Josse, Julie [Auteur]
Conference title :
Working Group on Model-Based Clustering Summer Session
City :
Vienne
Country :
Autriche
Start date of the conference :
2019-07-14
HAL domain(s) :
Statistiques [stat]/Méthodologie [stat.ME]
English abstract : [en]
Since the 90s, model-based clustering is largely used to classify data. Nowadays, with the increase of available data, missing values are more frequent. We defend the need to embed the missingness mechanism directly within ...
Show more >Since the 90s, model-based clustering is largely used to classify data. Nowadays, with the increase of available data, missing values are more frequent. We defend the need to embed the missingness mechanism directly within the clustering model-ing step. There exist three types of missing data: missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). In all situations , logistic regression is proposed as a natural and exible candidate model. In this unied context, standard model selection criteria can be used to select between such dierent missing data mechanisms, simultaneously with the number of clusters. Practical interest of our proposal is illustrated on data derived from medical studies suffering from many missing data.Show less >
Show more >Since the 90s, model-based clustering is largely used to classify data. Nowadays, with the increase of available data, missing values are more frequent. We defend the need to embed the missingness mechanism directly within the clustering model-ing step. There exist three types of missing data: missing completely at random (MCAR), missing at random (MAR) and missing not at random (MNAR). In all situations , logistic regression is proposed as a natural and exible candidate model. In this unied context, standard model selection criteria can be used to select between such dierent missing data mechanisms, simultaneously with the number of clusters. Practical interest of our proposal is illustrated on data derived from medical studies suffering from many missing data.Show less >
Language :
Anglais
Audience :
Internationale
Popular science :
Non
Submission date :
2020-06-08T14:11:21Z
2020-06-09T09:16:50Z
2020-06-09T09:16:50Z
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