Improved robust model selection methods for a Lévy nonparametric regression in continuous time

Improved robust model selection methods for a Lévy nonparametric regression in continuous time

Bibliographic Details
Parent link:Journal of Nonparametric Statistics
Vol. 31, iss. 3.— 2019.— [P. 612-628]
Main Author: Pchelintsev E. A. Evgeny Anatoljevich
Corporate Author: Национальный исследовательский Томский политехнический университет Школа базовой инженерной подготовки Отделение математики и информатики
Other Authors: Pchelintsev V. A. Valery Anatoljevich, Pergamenshchikov S. M. Sergey Markovich
Summary: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
Language:English
Published: 2019
Subjects:
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
Online Access:https://doi.org/10.1080/10485252.2019.1609672
Format: Electronic Book Chapter
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=664080

Internet

https://doi.org/10.1080/10485252.2019.1609672

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