Bootstrap Validation of the Estimated Parameters in Mixture Models Used for Clustering

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ID Serval
serval:BIB_574E6CA15653
Type
Article: article d'un périodique ou d'un magazine.
Collection
Publications
Titre
Bootstrap Validation of the Estimated Parameters in Mixture Models Used for Clustering
Périodique
Journal de la Société Française de Statistique
Auteur(s)
Taushanov Zhivko, Berchtold André
ISSN
2102-6238
Statut éditorial
Publié
Date de publication
20/03/2019
Volume
160
Numéro
1
Pages
114-129
Langue
anglais
Résumé
When a mixture model is used to perform clustering, the uncertainty is related both to the choice of an optimal model (including the number of clusters) and to the estimation of the parameters. We discuss here the computation of confidence intervals using different bootstrap approaches, which either mix or separate the two kinds of uncertainty. In particular, we suggest two new approaches that rely to some degree on the model specification considered as optimal by the researcher, and that address specifically the uncertainty related to parameter estimation. These methods are especially useful for poorly separated data or complex models, where the selected solution is difficult to recreate in each bootstrap sample, and they present the advantage of reducing the well-known label-switching issue. Two simulation experiments based on the Hidden Mixture Transition Distribution model for the clustering of longitudinal data illustrate our proposed bootstrap approaches.
Mots-clé
clustering, mixture model, bootstrap, uncertainty, label-switching, confidence interval, frequentist estimation, HMTD model
Création de la notice
12/09/2019 9:37
Dernière modification de la notice
13/09/2019 7:08
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