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

Detaylı Bibliyografya
Parent link:Journal of Nonparametric Statistics
Vol. 31, iss. 3.— 2019.— [P. 612-628]
Yazar: Pchelintsev E. A. Evgeny Anatoljevich
Müşterek Yazar: Национальный исследовательский Томский политехнический университет Школа базовой инженерной подготовки Отделение математики и информатики
Diğer Yazarlar: Pchelintsev V. A. Valery Anatoljevich, Pergamenshchikov S. M. Sergey Markovich
Özet: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
Dil:İngilizce
Baskı/Yayın Bilgisi: 2019
Konular:
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 Erişim:https://doi.org/10.1080/10485252.2019.1609672
Materyal Türü: Elektronik Kitap Bölümü
KOHA link:https://koha.lib.tpu.ru/cgi-bin/koha/opac-detail.pl?biblionumber=664080
Diğer Bilgiler
Özet: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
DOI:10.1080/10485252.2019.1609672

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