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On the Asymptotic Approach to the Change-Point Problem and Exponential Convergence Rate in the Ergodic Theorem for Markov Chains Научная публикация

Журнал Theory of Probability and its Applications
ISSN: 0040-585X , E-ISSN: 1095-7219
Вых. Данные Год: 2023, Том: 68, Номер: 3, Страницы: 456-482 Страниц : 27 DOI: 10.1137/S0040585X97T991519
Ключевые слова change-point problem, change-point detection, delay time, number of “false alarms”, Poisson approximation, Markov chain with a positive atom, exponential convergence rate, asymptotically optimal solution
Авторы Borovkov A.A. 1
Организации
1 Sobolev Institute of Mathematics of the Siberian Branch of the Russian Academy of Sciences

Информация о финансировании (1)

1 Институт математики им. С.Л. Соболева СО РАН FWNF-2022-0010

Реферат: Under the assumption that the change-point time is large, a Poisson approximation for the distribution of the number of false alarms is obtained. We also find upper bounds for the probability of a "false alarm” on a given time interval. An asymptotic expansion for the mean delay time of the alarm signal relative to the change-point time is obtained. To get this result, we establish the exponential convergence rate in the ergodic theorem for Markov chains with a positive atom; chains of this kind describe the monitoring of control systems. A game-theoretic approach is employed to obtain asymptotically optimal solutions of the change-point problem.
Библиографическая ссылка: Borovkov A.A.
On the Asymptotic Approach to the Change-Point Problem and Exponential Convergence Rate in the Ergodic Theorem for Markov Chains
Theory of Probability and its Applications. 2023. V.68. N3. P.456-482. DOI: 10.1137/S0040585X97T991519 WOS Scopus РИНЦ OpenAlex
Оригинальная: Borovkov A.A.
Об асимптотическом подходе к задаче о разладке и экспоненциальной сходимости в эргодической теореме для цепей Маркова
Теория вероятностей и ее применения. 2023. Т.68. №3. С.456-482. DOI: 10.4213/tvp5642 РИНЦ OpenAlex
Даты:
Опубликована в печати: 30 нояб. 2023 г.
Опубликована online: 30 нояб. 2023 г.
Идентификаторы БД:
Web of science: WOS:001108619900012
Scopus: 2-s2.0-85179371498
РИНЦ: 64001722
OpenAlex: W4388464845
Цитирование в БД:
БД Цитирований
OpenAlex 2
Scopus 1
Web of science 1
Альметрики: