Constraining kernel estimators in ...
Document type :
Compte-rendu et recension critique d'ouvrage
Title :
Constraining kernel estimators in semiparametric copula mixture models
Author(s) :
Mazo, Gildas [Auteur correspondant]
Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] [MaIAGE]
Université Paris Saclay (COmUE)
Averyanov, Yaroslav [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Mathématiques et Informatique Appliquées du Génome à l'Environnement [Jouy-En-Josas] [MaIAGE]
Université Paris Saclay (COmUE)
Averyanov, Yaroslav [Auteur]
MOdel for Data Analysis and Learning [MODAL]
Journal title :
Computational Statistics and Data Analysis
Pages :
170-189
Publisher :
Elsevier
Publication date :
2019
ISSN :
0167-9473
Keyword(s) :
Copula
Semiparametric
Nonparametric
Mixture model
Semiparametric
Nonparametric
Mixture model
English keyword(s) :
Kernel
Clustering
Clustering
HAL domain(s) :
Mathématiques [math]
Informatique [cs]
Sciences du Vivant [q-bio]
Informatique [cs]
Sciences du Vivant [q-bio]
English abstract : [en]
A novel algorithm for performing inference and/or clustering in semiparametric copula-based mixture models is presented. The standard kernel density estimator is replaced by a weighted version that permits to take into ...
Show more >A novel algorithm for performing inference and/or clustering in semiparametric copula-based mixture models is presented. The standard kernel density estimator is replaced by a weighted version that permits to take into account the constraints put on the underlying marginal densities. Lower misclassification error rates and better estimates are obtained on simulations. The pointwise consistency of the weighted kernel density estimator is established under an assumption on the rate of convergence of the sample maximum.Show less >
Show more >A novel algorithm for performing inference and/or clustering in semiparametric copula-based mixture models is presented. The standard kernel density estimator is replaced by a weighted version that permits to take into account the constraints put on the underlying marginal densities. Lower misclassification error rates and better estimates are obtained on simulations. The pointwise consistency of the weighted kernel density estimator is established under an assumption on the rate of convergence of the sample maximum.Show less >
Language :
Anglais
Popular science :
Non
Collections :
Source :
Files
- document
- Open access
- Access the document
- S0167947319300945.pdf
- Open access
- Access the document