On large deviations in estimation problem with dependent observations

The paper is devoted to the stochastic optimization problem with a stationary ergodic random sequence satisfying the hypermixing condition. It is assumed that we have the finite number of observed elements in the sequence, and instead of solving the former problem we investigate the empirical func...

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Збережено в:
Бібліографічні деталі
Дата:2005
Автори: Knopov, P.S., Kasitskaya, E.J.
Формат: Стаття
Мова:English
Опубліковано: Інститут математики НАН України 2005
Онлайн доступ:https://nasplib.isofts.kiev.ua/handle/123456789/4430
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Назва журналу:Digital Library of Periodicals of National Academy of Sciences of Ukraine
Цитувати:On large deviations in estimation problem with dependent observations / P.S. Knopov, E.J. Kasitskaya // Theory of Stochastic Processes. — 2005. — Т. 11 (27), № 3-4. — С. 97–103. — Бібліогр.: 4 назв.— англ.

Репозитарії

Digital Library of Periodicals of National Academy of Sciences of Ukraine
id nasplib_isofts_kiev_ua-123456789-4430
record_format dspace
spelling Knopov, P.S.
Kasitskaya, E.J.
2009-11-09T15:35:24Z
2009-11-09T15:35:24Z
2005
On large deviations in estimation problem with dependent observations / P.S. Knopov, E.J. Kasitskaya // Theory of Stochastic Processes. — 2005. — Т. 11 (27), № 3-4. — С. 97–103. — Бібліогр.: 4 назв.— англ.
0321-3900
https://nasplib.isofts.kiev.ua/handle/123456789/4430
519.21
The paper is devoted to the stochastic optimization problem with a stationary ergodic random sequence satisfying the hypermixing condition. It is assumed that we have the finite number of observed elements in the sequence, and instead of solving the former problem we investigate the empirical function, find its points of minimum, and study their asymptotic properties. More precisely we consider the probabilities of large deviations of minimizers and the minimal value of the empirical criterion function from the corresponding characteristics of the main problem. The conditions under which the probabilities of the large deviations decrease exponentially are found.
en
Інститут математики НАН України
On large deviations in estimation problem with dependent observations
Article
published earlier
institution Digital Library of Periodicals of National Academy of Sciences of Ukraine
collection DSpace DC
title On large deviations in estimation problem with dependent observations
spellingShingle On large deviations in estimation problem with dependent observations
Knopov, P.S.
Kasitskaya, E.J.
title_short On large deviations in estimation problem with dependent observations
title_full On large deviations in estimation problem with dependent observations
title_fullStr On large deviations in estimation problem with dependent observations
title_full_unstemmed On large deviations in estimation problem with dependent observations
title_sort on large deviations in estimation problem with dependent observations
author Knopov, P.S.
Kasitskaya, E.J.
author_facet Knopov, P.S.
Kasitskaya, E.J.
publishDate 2005
language English
publisher Інститут математики НАН України
format Article
description The paper is devoted to the stochastic optimization problem with a stationary ergodic random sequence satisfying the hypermixing condition. It is assumed that we have the finite number of observed elements in the sequence, and instead of solving the former problem we investigate the empirical function, find its points of minimum, and study their asymptotic properties. More precisely we consider the probabilities of large deviations of minimizers and the minimal value of the empirical criterion function from the corresponding characteristics of the main problem. The conditions under which the probabilities of the large deviations decrease exponentially are found.
issn 0321-3900
url https://nasplib.isofts.kiev.ua/handle/123456789/4430
citation_txt On large deviations in estimation problem with dependent observations / P.S. Knopov, E.J. Kasitskaya // Theory of Stochastic Processes. — 2005. — Т. 11 (27), № 3-4. — С. 97–103. — Бібліогр.: 4 назв.— англ.
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first_indexed 2025-12-07T18:10:50Z
last_indexed 2025-12-07T18:10:50Z
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