Relative error prediction in nonparametric deconvolution regression model

Thiam, Baba

Type de document :

Compte-rendu et recension critique d'ouvrage

DOI :

10.1111/stan.12135

Titre :

Relative error prediction in nonparametric deconvolution regression model

Auteur(s) :

Thiam, Baba [Auteur]

Lille économie management - UMR 9221 [LEM]

Titre de la revue :

Statistica Neerlandica

Éditeur :

Wiley

Date de publication :

2018-04-15

ISSN :

0039-0402

Discipline(s) HAL :

Sciences de l'Homme et Société/Méthodes et statistiques

Résumé en anglais : [en]

In this paper, we studied an alternative estimator of the regression function when the covariates are observed with error. It is based on the minimization of the relative mean squared error. We obtain expressions for its ...
Lire la suite >In this paper, we studied an alternative estimator of the regression function when the covariates are observed with error. It is based on the minimization of the relative mean squared error. We obtain expressions for its asymptotic bias and variance together with an asymptotic normality result. Our technique is illustrated on simulation studies. Numerical results suggest that the studied estimator can lead to tangible improvements in prediction over the usual kernel deconvolution regression estimator, particularly in the presence of several outliers in the dataset.Lire moins >

Langue :

Anglais

Vulgarisation :

Non

Collections :

Lille Économie Management (LEM) - UMR 9221

Source :

Harvested from HAL

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