Standard

Linear regression and filtering under nonstandard assumptions (arbitrary noise). / Granichin, O.N.

в: IEEE Transactions on Automatic Control, Том 49, № 10, 10.2004, стр. 1830-1835.

Результаты исследований: Научные публикации в периодических изданиях › статья › Рецензирование

Harvard

Granichin, ON 2004, 'Linear regression and filtering under nonstandard assumptions (arbitrary noise)', IEEE Transactions on Automatic Control, Том. 49, № 10, стр. 1830-1835. https://doi.org/10.1109/TAC.2004.835585

APA

Granichin, O. N. (2004). Linear regression and filtering under nonstandard assumptions (arbitrary noise). IEEE Transactions on Automatic Control, 49(10), 1830-1835. https://doi.org/10.1109/TAC.2004.835585

Vancouver

Granichin ON. Linear regression and filtering under nonstandard assumptions (arbitrary noise). IEEE Transactions on Automatic Control. 2004 Окт.;49(10):1830-1835. https://doi.org/10.1109/TAC.2004.835585

Author

Granichin, O.N. / Linear regression and filtering under nonstandard assumptions (arbitrary noise). в: IEEE Transactions on Automatic Control. 2004 ; Том 49, № 10. стр. 1830-1835.

BibTeX

@article{61dbcdc305614e42aac68818f784ad91,
title = "Linear regression and filtering under nonstandard assumptions (arbitrary noise)",
abstract = "This note is devoted to parameter estimation in linear regression and filtering, where the observation noise does not possess any {"}nice{"} probabilistic properties. In particular, the noise might have an {"}Unknown-but-bounded{"} deterministic nature. The basic assumption is that the model regressors (inputs) are random. Optimal rates of convergence for the modified stochastic approximation and least-squares algorithms are established under some weak assumptions. Typical behavior of the algorithms in the presence of such deterministic noise is illustrated by numerical examples.",
keywords = "filtering, linear regression, parameter estimation, prediction, randomized algorithm, STOCHASTIC-APPROXIMATION, SYSTEMS, CONVERGENCE, ALGORITHMS, STABILITY",
author = "O.N. Granichin",
year = "2004",
month = oct,
doi = "10.1109/TAC.2004.835585",
language = "Английский",
volume = "49",
pages = "1830--1835",
journal = "IEEE Transactions on Automatic Control",
issn = "0018-9286",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
number = "10",

}

RIS

TY - JOUR

T1 - Linear regression and filtering under nonstandard assumptions (arbitrary noise)

AU - Granichin, O.N.

PY - 2004/10

Y1 - 2004/10

N2 - This note is devoted to parameter estimation in linear regression and filtering, where the observation noise does not possess any "nice" probabilistic properties. In particular, the noise might have an "Unknown-but-bounded" deterministic nature. The basic assumption is that the model regressors (inputs) are random. Optimal rates of convergence for the modified stochastic approximation and least-squares algorithms are established under some weak assumptions. Typical behavior of the algorithms in the presence of such deterministic noise is illustrated by numerical examples.

AB - This note is devoted to parameter estimation in linear regression and filtering, where the observation noise does not possess any "nice" probabilistic properties. In particular, the noise might have an "Unknown-but-bounded" deterministic nature. The basic assumption is that the model regressors (inputs) are random. Optimal rates of convergence for the modified stochastic approximation and least-squares algorithms are established under some weak assumptions. Typical behavior of the algorithms in the presence of such deterministic noise is illustrated by numerical examples.

KW - filtering

KW - linear regression

KW - parameter estimation

KW - prediction

KW - randomized algorithm

KW - STOCHASTIC-APPROXIMATION

KW - SYSTEMS

KW - CONVERGENCE

KW - ALGORITHMS

KW - STABILITY

U2 - 10.1109/TAC.2004.835585

DO - 10.1109/TAC.2004.835585

M3 - статья

VL - 49

SP - 1830

EP - 1835

JO - IEEE Transactions on Automatic Control

JF - IEEE Transactions on Automatic Control

SN - 0018-9286

IS - 10

ER -

ID: 5014758