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On detecting alternatives by one-parametric recursive residuals. / Sakhanenko, A. I.

в: Siberian Electronic Mathematical Reports, Том 19, № 1, 2022, стр. 292-308.

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

Harvard

Sakhanenko, AI 2022, 'On detecting alternatives by one-parametric recursive residuals', Siberian Electronic Mathematical Reports, Том. 19, № 1, стр. 292-308. https://doi.org/10.33048/semi.2022.19.024

APA

Sakhanenko, A. I. (2022). On detecting alternatives by one-parametric recursive residuals. Siberian Electronic Mathematical Reports, 19(1), 292-308. https://doi.org/10.33048/semi.2022.19.024

Vancouver

Sakhanenko AI. On detecting alternatives by one-parametric recursive residuals. Siberian Electronic Mathematical Reports. 2022;19(1):292-308. doi: 10.33048/semi.2022.19.024

Author

Sakhanenko, A. I. / On detecting alternatives by one-parametric recursive residuals. в: Siberian Electronic Mathematical Reports. 2022 ; Том 19, № 1. стр. 292-308.

BibTeX

@article{d0e3a39d2ba749e0be4429c419da926d,
title = "On detecting alternatives by one-parametric recursive residuals",
abstract = "We consider a linear regression model with one unknown parameter which is estimated by the least squares method. We suppose that, in reality, the given observations satisfy a close alternative to the linear regression model. We investigate the limiting behaviour of the normalized process of sums of recursive residuals. Such residuals were introduced by Brown, Durbin and Evans (1975) and their sums are a convenient tool for detecting discrepancy between observations and the studied model. In particular, under less restrictive assumptions we generalize a key result from Bischoff (2016).",
keywords = "Close alternative, Linear regression, Recursive residuals, Weak convergence, Wiener process",
author = "Sakhanenko, {A. I.}",
note = "Funding Information: Sakhanenko, A.I., On detecting alternatives by one-parametric recursive residuals. {\textcopyright} 2022 Sakhanenko A.I. The work is supported by Mathematical Center in Akademgorodok under agreement No. 075-15-2022-282 with the Ministry of Science and Higher Education of the Russian Federation. Received October, 4, 2021, published May, 30, 2022. Publisher Copyright: {\textcopyright} 2022. Sakhanenko A.I.",
year = "2022",
doi = "10.33048/semi.2022.19.024",
language = "English",
volume = "19",
pages = "292--308",
journal = "Сибирские электронные математические известия",
issn = "1813-3304",
publisher = "Sobolev Institute of Mathematics",
number = "1",

}

RIS

TY - JOUR

T1 - On detecting alternatives by one-parametric recursive residuals

AU - Sakhanenko, A. I.

N1 - Funding Information: Sakhanenko, A.I., On detecting alternatives by one-parametric recursive residuals. © 2022 Sakhanenko A.I. The work is supported by Mathematical Center in Akademgorodok under agreement No. 075-15-2022-282 with the Ministry of Science and Higher Education of the Russian Federation. Received October, 4, 2021, published May, 30, 2022. Publisher Copyright: © 2022. Sakhanenko A.I.

PY - 2022

Y1 - 2022

N2 - We consider a linear regression model with one unknown parameter which is estimated by the least squares method. We suppose that, in reality, the given observations satisfy a close alternative to the linear regression model. We investigate the limiting behaviour of the normalized process of sums of recursive residuals. Such residuals were introduced by Brown, Durbin and Evans (1975) and their sums are a convenient tool for detecting discrepancy between observations and the studied model. In particular, under less restrictive assumptions we generalize a key result from Bischoff (2016).

AB - We consider a linear regression model with one unknown parameter which is estimated by the least squares method. We suppose that, in reality, the given observations satisfy a close alternative to the linear regression model. We investigate the limiting behaviour of the normalized process of sums of recursive residuals. Such residuals were introduced by Brown, Durbin and Evans (1975) and their sums are a convenient tool for detecting discrepancy between observations and the studied model. In particular, under less restrictive assumptions we generalize a key result from Bischoff (2016).

KW - Close alternative

KW - Linear regression

KW - Recursive residuals

KW - Weak convergence

KW - Wiener process

UR - http://www.scopus.com/inward/record.url?scp=85132565540&partnerID=8YFLogxK

U2 - 10.33048/semi.2022.19.024

DO - 10.33048/semi.2022.19.024

M3 - Article

AN - SCOPUS:85132565540

VL - 19

SP - 292

EP - 308

JO - Сибирские электронные математические известия

JF - Сибирские электронные математические известия

SN - 1813-3304

IS - 1

ER -

ID: 36452817