Matrix parameter estimation in an autoregression model

The vector difference equation ξk = Af(ξk−1)+εk, where (εk) is a square integrable difference martingale, is considered. A family of estimators ˇAn depending, besides the sample size n, on a bounded Lipschitz function is constructed. Convergence in distribution of √n (ˇAn − A) as n→∞is proved wit...

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Дата:2006
Автори: Yurachkivsky, A.P., Ivanenko, D.O.
Формат: Стаття
Мова:English
Опубліковано: Інститут математики НАН України 2006
Онлайн доступ:http://dspace.nbuv.gov.ua/handle/123456789/4450
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Назва журналу:Digital Library of Periodicals of National Academy of Sciences of Ukraine
Цитувати:Matrix parameter estimation in an autoregression model / A.P. Yurachkivsky, D.O. Ivanenko // Theory of Stochastic Processes. — 2006. — Т. 12 (28), № 1-2. — С. 154–161. — Бібліогр.: 4 назв.— англ.

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Digital Library of Periodicals of National Academy of Sciences of Ukraine
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spelling irk-123456789-44502009-11-11T12:00:27Z Matrix parameter estimation in an autoregression model Yurachkivsky, A.P. Ivanenko, D.O. The vector difference equation ξk = Af(ξk−1)+εk, where (εk) is a square integrable difference martingale, is considered. A family of estimators ˇAn depending, besides the sample size n, on a bounded Lipschitz function is constructed. Convergence in distribution of √n (ˇAn − A) as n→∞is proved with the use of stochastic calculus. Ergodicity and even stationarity of (εk) is not assumed, so the limiting distribution may be, as the example shows, other than normal. 2006 Article Matrix parameter estimation in an autoregression model / A.P. Yurachkivsky, D.O. Ivanenko // Theory of Stochastic Processes. — 2006. — Т. 12 (28), № 1-2. — С. 154–161. — Бібліогр.: 4 назв.— англ. 0321-3900 http://dspace.nbuv.gov.ua/handle/123456789/4450 519.21 en Інститут математики НАН України
institution Digital Library of Periodicals of National Academy of Sciences of Ukraine
collection DSpace DC
language English
description The vector difference equation ξk = Af(ξk−1)+εk, where (εk) is a square integrable difference martingale, is considered. A family of estimators ˇAn depending, besides the sample size n, on a bounded Lipschitz function is constructed. Convergence in distribution of √n (ˇAn − A) as n→∞is proved with the use of stochastic calculus. Ergodicity and even stationarity of (εk) is not assumed, so the limiting distribution may be, as the example shows, other than normal.
format Article
author Yurachkivsky, A.P.
Ivanenko, D.O.
spellingShingle Yurachkivsky, A.P.
Ivanenko, D.O.
Matrix parameter estimation in an autoregression model
author_facet Yurachkivsky, A.P.
Ivanenko, D.O.
author_sort Yurachkivsky, A.P.
title Matrix parameter estimation in an autoregression model
title_short Matrix parameter estimation in an autoregression model
title_full Matrix parameter estimation in an autoregression model
title_fullStr Matrix parameter estimation in an autoregression model
title_full_unstemmed Matrix parameter estimation in an autoregression model
title_sort matrix parameter estimation in an autoregression model
publisher Інститут математики НАН України
publishDate 2006
url http://dspace.nbuv.gov.ua/handle/123456789/4450
citation_txt Matrix parameter estimation in an autoregression model / A.P. Yurachkivsky, D.O. Ivanenko // Theory of Stochastic Processes. — 2006. — Т. 12 (28), № 1-2. — С. 154–161. — Бібліогр.: 4 назв.— англ.
work_keys_str_mv AT yurachkivskyap matrixparameterestimationinanautoregressionmodel
AT ivanenkodo matrixparameterestimationinanautoregressionmodel
first_indexed 2023-03-24T08:30:13Z
last_indexed 2023-03-24T08:30:13Z
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