Parsimonious models of multivariate binary time series: statistical estimation and forecasting

Shibalko, Siarhei; Kharin, Yuriy

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Please use this identifier to cite or link to this item: https://elib.bsu.by/handle/123456789/306255

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DC Field	Value	Language
dc.contributor.author	Shibalko, Siarhei
dc.contributor.author	Kharin, Yuriy
dc.date.accessioned	2023-12-12T12:42:18Z	-
dc.date.available	2023-12-12T12:42:18Z	-
dc.date.issued	2023
dc.identifier.citation	Pattern Recognition and Information Processing (PRIP’2023). Artificial Universe: New Horisont : Proceedings of the 16 th International Conference, Belarus, Minsk, October 17–19, 2023 / Belarusian State University : eds. A. Nedzved, A. Belotserkovsky. – Minsk : BSU, 2023. – Pp. 296-299.
dc.identifier.isbn	978-985-881-522-6
dc.identifier.uri	https://elib.bsu.by/handle/123456789/306255	-
dc.description.abstract	This paper is devoted to parsimonious models of multivariate binary time series. Consistent asymptotically normal statistical estimators for the parameters of proposed parsimonious models are constructed. Algorithms for statistical estimation of model parameters and forecasting of future states of time series are presented. Results of computer experiments on simulated and real statistical discrete-valued data are given
dc.language.iso	en
dc.publisher	Minsk : BSU
dc.rights	info:eu-repo/semantics/openAccess
dc.subject	ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Кибернетика
dc.subject	ЭБ БГУ::ЕСТЕСТВЕННЫЕ И ТОЧНЫЕ НАУКИ::Математика
dc.title	Parsimonious models of multivariate binary time series: statistical estimation and forecasting
dc.type	conference paper
Appears in Collections:	2023. Pattern Recognition and Information Processing (PRIP’2023). Artificial Intelliverse: Expanding Horizons

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