Improved robust model selection methods for a Lévy nonparametric regression in continuous time; Journal of Nonparametric Statistics; Vol. 31, iss. 3

Improved robust model selection methods for a Lévy nonparametric regression in continuous time; Journal of Nonparametric Statistics; Vol. 31, iss. 3

Détails bibliographiques
Parent link:	Journal of Nonparametric Statistics Vol. 31, iss. 3.— 2019.— [P. 612-628]
Auteur principal:	Pchelintsev E. A. Evgeny Anatoljevich
Collectivité auteur:	Национальный исследовательский Томский политехнический университет Школа базовой инженерной подготовки Отделение математики и информатики
Autres auteurs:	Pchelintsev V. A. Valery Anatoljevich, Pergamenshchikov S. M. Sergey Markovich
Résumé:	Title screen In this paper, we develop the James-Stein improved method for the estimation problem of a nonparametric periodic function observed with Lévy noises in continuous time. An adaptive model selection procedure based on the weighted improved least squares estimates is constructed. The improvement effect for nonparametric models is studied. It turns out that in non-asymptotic setting the accuracy improvement for nonparametric models is more important than for parametric ones. Moreover, sharp oracle inequalities for the robust risks have been shown and the adaptive efficiency property for the proposed procedures has been established. The numerical simulations are given. Режим доступа: по договору с организацией-держателем ресурса
Langue:	anglais
Publié:	2019
Sujets:	электронный ресурс труды учёных ТПУ improved non-asymptotic estimation James–Stein procedures robust quadratic risk nonparametric regression Lйvy process model selection sharp oracle inequality adaptive estimation asymptotic efficiency регрессия асимптотическая эффективность процессы Леви
Accès en ligne:	https://doi.org/10.1080/10485252.2019.1609672
Format:	Électronique Chapitre de livre
KOHA link:	https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=664080

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