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Autoregressive moving average model for matrix time series

Shujin Wu ,

College of Liberal Arts and Sciences, China University of Petroleum-Beijing at Karamay, Karamay, People's Republic of China; KLATASDS-MOE, School of Statistics, East China Normal University, Shanghai, People's Republic of China

Ping Bi

School of Mathematical Sciences, Key Laboratory of Pure Mathematics and Mathematical Practice, East China Normal University, Shanghai, People's Republic of China

Pages | Received 08 Dec. 2022, Accepted 18 Sep. 2023, Published online: 04 Oct. 2023,
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In the paper, the autoregressive moving average model for matrix time series (MARMA) is investigated. The properties of the MARMA model are investigated by using the conditional least square estimation, the conditional maximum likelihood estimation, the projection theorem in Hilbert space and the decomposition technique of time series, which include necessary and sufficient conditions for stationarity and invertibility, model parameter estimation, model testing and model forecasting.


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To cite this article: Shujin Wu & Ping Bi (04 Oct 2023): Autoregressive moving average model for matrix time series, Statistical Theory and Related Fields, DOI: 10.1080/24754269.2023.2262360 To link to this article: