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On the asymptotic optimality of orthoregressional estimators Научная публикация

Журнал

Journal of Applied and Industrial Mathematics
ISSN: 1990-4789 , E-ISSN: 1990-4797

Вых. Данные

Год: 2016, Том: 10, Номер: 4, Страницы: 511-519 Страниц : 9 DOI: 10.1134/S1990478916040074

Ключевые слова

asymptotic efficiency; linear autonomous difference equation; orthoregressional estimator; parameter identification; STLS estimator

Авторы

Lomov A.A. ^1,2

Организации

1	Sobolev Institute of Mathematics, pr. Akad. Koptyuga 4, Novosibirsk, 630090, Russian Federation
2	Novosibirsk State University, ul. Pirogova 2, Novosibirsk, 630090, Russian Federation

It is shown that the orthoregressional (STLS) parameter estimators in linear algebraic systems (including autonomous difference equations with matrix coefficients) converge to the maximum likelihood estimators and thus become asymptotically best in the limit case of large variances of the random coordinates on the variety of solutions to the system observed with additive random perturbations. © 2016, Pleiades Publishing, Ltd.

Lomov A.A.
On the asymptotic optimality of orthoregressional estimators
Journal of Applied and Industrial Mathematics. 2016. V.10. N4. P.511-519. DOI: 10.1134/S1990478916040074 Scopus OpenAlex

Ломов А.А.
Об асимптотической оптимальности орторегрессионных оценок
Сибирский журнал индустриальной математики. 2016. Т.19. №4(68). С.51-60. DOI: 10.17377/sibjim.2016.19.406

Scopus:	2-s2.0-84996587684
OpenAlex:	W2549429725

БД	Цитирований
Scopus	3
OpenAlex	2