Robust signal processing in nonparametric autoregression

Autor:	Maria Povzun, Evgeny Pchelintsev
Rok vydání:	2020
Předmět:	History Autoregressive model Computer science Kernel (statistics) Model selection Ordinary least squares Monte Carlo method Nonparametric statistics Estimator Sequential model Algorithm Computer Science Applications Education
Zdroj:	Journal of Physics: Conference Series. 1611:012060
ISSN:	1742-6596 1742-6588
DOI:	10.1088/1742-6596/1611/1/012060
Popis:	In this paper we consider the problem of a robust adaptive estimation of a periodic signal modeled by the nonparametric autoregression. We develop a new sequential model selection method, using improved estimation approach and the efficient sequential kernel estimators. This procedure is based on the sequential estimators. For robust quadratic risk of proposed estimate we obtain sharp oracle inequality that allows us to establish the efficiency property of this model selection procedure. We give the Monte Carlo simulation results for numerical comparing of the risks of proposed improved procedure and ordinary least squares estimates.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::04e8c057d7b6795c0ebde43dcdad271c https://doi.org/10.1088/1742-6596/1611/1/012060 Zobrazit plný text záznamu