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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Datum:2006
Hauptverfasser: Yurachkivsky, A.P., Ivanenko, D.O.
Format: Artikel
Sprache:English
Veröffentlicht: Інститут математики НАН України 2006
Online Zugang:https://nasplib.isofts.kiev.ua/handle/123456789/4450
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Назва журналу:Digital Library of Periodicals of National Academy of Sciences of Ukraine
Zitieren: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
id nasplib_isofts_kiev_ua-123456789-4450
record_format dspace
spelling Yurachkivsky, A.P.
Ivanenko, D.O.
2009-11-10T14:54:04Z
2009-11-10T14:54:04Z
2006
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
https://nasplib.isofts.kiev.ua/handle/123456789/4450
519.21
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.
en
Інститут математики НАН України
Matrix parameter estimation in an autoregression model
Article
published earlier
institution Digital Library of Periodicals of National Academy of Sciences of Ukraine
collection DSpace DC
title Matrix parameter estimation in an autoregression model
spellingShingle Matrix parameter estimation in an autoregression model
Yurachkivsky, A.P.
Ivanenko, D.O.
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
author Yurachkivsky, A.P.
Ivanenko, D.O.
author_facet Yurachkivsky, A.P.
Ivanenko, D.O.
publishDate 2006
language English
publisher Інститут математики НАН України
format Article
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.
issn 0321-3900
url https://nasplib.isofts.kiev.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 2025-12-01T15:40:45Z
last_indexed 2025-12-01T15:40:45Z
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