A k-nearest neighbor approach for functional regression - Laboratoire Jean-Alexandre Dieudonné Accéder directement au contenu
Article Dans Une Revue Statistics and Probability Letters Année : 2008

A k-nearest neighbor approach for functional regression

Résumé

Let (X, Y) be a random pair taking values in H × R, where H is an infinite dimensional separable Hilbert space. We establish weak consistency of a nearest neighbor-type estimator of the regression function of Y on X based on independent observations of the pair (X, Y). As a general strategy, we propose to reduce the infinite dimension of H by considering only the first d coefficients of an expansion of X in an orthonormal system of H, and then to perform k-nearest neighbor regression in R^d. Both the dimension and the number of neighbors are automatically selected from the observations using a simple data-dependent splitting device.
Fichier principal
Vignette du fichier
regression_ppv.pdf (111.12 Ko) Télécharger le fichier
Origine : Fichiers produits par l'(les) auteur(s)
Loading...

Dates et versions

hal-01292692 , version 1 (23-03-2016)

Identifiants

Citer

Thomas Laloë. A k-nearest neighbor approach for functional regression. Statistics and Probability Letters, 2008, 78 (10), pp.1189-1193. ⟨10.1016/j.spl.2007.11.014⟩. ⟨hal-01292692⟩
46 Consultations
154 Téléchargements

Altmetric

Partager

Gmail Facebook X LinkedIn More