
Numerical performance of Penalized Comparison to Overfitting for multivariate kernel density estimation
Varet, Suzanne; Lacour, Claire; Massart, Pascal; Rivoirard, Vincent (2023), Numerical performance of Penalized Comparison to Overfitting for multivariate kernel density estimation, ESAIM. Probability and Statistics, 27, p. 621 - 667. 10.1051/ps/2022018
View/ Open
Type
Article accepté pour publication ou publiéDate
2023Journal name
ESAIM. Probability and StatisticsVolume
27Publisher
EDP Sciences
Published in
Paris
Pages
621 - 667
Publication identifier
Metadata
Show full item recordAuthor(s)
Varet, SuzanneLaboratoire de Mathématiques d'Orsay [LMO]
Lacour, Claire
Laboratoire Analyse et Mathématiques Appliquées [LAMA]
Massart, Pascal
Statistique mathématique et apprentissage [CELESTE]
Rivoirard, Vincent
CEntre de REcherches en MAthématiques de la DEcision [CEREMADE]
Abstract (EN)
Kernel density estimation is a well known method involving a smoothing parameter (the bandwidth) that needs to be tuned by the user. Although this method has been widely used the bandwidth selection remains a challenging issue in terms of balancing algorithmic performance and statistical relevance. The purpose of this paper is to compare a recently developped bandwidth selection method for kernel density estimation to those which are commonly used by now (at least those which are implemented in the R-package). This new method is called Penalized Comparison to Overfitting (PCO). It has been proposed by some of the authors of this paper in a previous work devoted to its statistical relevance from a purely theoretical perspective. It is compared here to other usual bandwidth selection methods for univariate and also multivariate kernel density estimation on the basis of intensive simulation studies. In particular, cross-validation and plug-in criteria are numerically investigated and compared to PCO. The take home message is that PCO can outperform the classical methods without algorithmic additionnal cost.Subjects / Keywords
Multivariate density estimation; Bandwidth selection; Kernel-based density estimationRelated items
Showing items related by title and author.
-
Lacour, Claire; Massart, Pascal; Rivoirard, Vincent (2017) Article accepté pour publication ou publié
-
Adaptive greedy algorithm for moderately large dimensions in kernel conditional density estimation Nguyen, Minh-Lien; Lacour, Claire; Rivoirard, Vincent (2022) Article accepté pour publication ou publié
-
Bertin, Karine; Lacour, Claire; Rivoirard, Vincent (2016) Article accepté pour publication ou publié
-
Bonnet, Anna; Lacour, Claire; Picard, Franck; Rivoirard, Vincent (2022) Article accepté pour publication ou publié
-
Donnet, Sophie; Rivoirard, Vincent; Rousseau, Judith (2020) Article accepté pour publication ou publié