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

Dades bibliogràfiques
Parent link:Journal of Nonparametric Statistics
Vol. 31, iss. 3.— 2019.— [P. 612-628]
Autor principal: Pchelintsev E. A. Evgeny Anatoljevich
Autor corporatiu: Национальный исследовательский Томский политехнический университет Школа базовой инженерной подготовки Отделение математики и информатики
Altres autors: Pchelintsev V. A. Valery Anatoljevich, Pergamenshchikov S. M. Sergey Markovich
Sumari: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
Idioma:anglès
Publicat: 2019
Matèries:
Accés en línia:https://doi.org/10.1080/10485252.2019.1609672
Format: Electrònic Capítol de llibre
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=664080

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