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ON DETECTING ALTERNATIVES BY ONE-PARAMETRIC RECURSIVE RESIDUALS Научная публикация

Журнал

Сибирские электронные математические известия (Siberian Electronic Mathematical Reports)
, E-ISSN: 1813-3304

Вых. Данные

Год: 2022, Том: 19, Номер: 1, Страницы: 292-308 Страниц : 17 DOI: 10.33048/semi.2022.19.024

Ключевые слова

Close alternative; Linear regression; Recursive residuals; Weak convergence; Wiener process

Авторы

Sakhanenko A.I. ^1,2

Организации

1	Novosibirsk State University, 2,Pirogova str., Novosibirsk, 630090, Russian Federation
2	Sobolev Institute of Mathematics, 4,Acad. Koptyug ave, Novosibirsk, 630090, Russian Federation

Информация о финансировании (1)

Математический центр в Академгородке

075-15-2019-1675

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). © 2022. Sakhanenko A.I.

Sakhanenko A.I.
ON DETECTING ALTERNATIVES BY ONE-PARAMETRIC RECURSIVE RESIDUALS
Сибирские электронные математические известия (Siberian Electronic Mathematical Reports). 2022. V.19. N1. P.292-308. DOI: 10.33048/semi.2022.19.024 WOS Scopus РИНЦ

Web of science:	WOS:000886649700002
Scopus:	2-s2.0-85132565540
РИНЦ:	49384635

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