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Please use this identifier to cite or link to this item: https://libeldoc.bsuir.by/handle/123456789/54352
Title: Parsimonious models of multivariate binary time series: statistical estimation and forecasting
Authors: Shibalko, S.
Kharin, Y.
Keywords: материалы конференций;binary time series;multivariate data;statistical estimation;parsimonious models;statistical forecasting
Issue Date: 2023
Publisher: BSU
Citation: Shibalko, S. Parsimonious models of multivariate binary time series: statistical estimation and forecasting / S. Shibalko, Y. Kharin // Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023) : Proceedings of the 16th International Conference, October 17–19, 2023, Minsk, Belarus / United Institute of Informatics Problems of the National Academy of Sciences of Belarus. – Minsk, 2023. – P. 296–299.
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.
URI: https://libeldoc.bsuir.by/handle/123456789/54352
Appears in Collections:Pattern Recognition and Information Processing (PRIP'2023) = Распознавание образов и обработка информации (2023)

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