Autoregressive moving average model for matrix time series  

作　　者：Shujin Wu Ping Bi 

机构地区：[1]College of Liberal Arts and Sciences,China University of Petroleum-Beijing at Karamay,Karamay,People’s Republic of China [2]KLATASDS-MOE,School of Statistics,East China Normal University,Shanghai,People’s Republic of China [3]School of Mathematical Sciences,Key Laboratory of Pure Mathematics and Mathematical Practice,East China Normal University,Shanghai,People’s Republic of China

出　　处：《Statistical Theory and Related Fields》2023年第4期318-335,共18页统计理论及其应用（英文）

基　　金：This paper is partially supported by the basic scientific research business expenses of Universities in Xinjiang,China[Grant Number XQZX20230057];the National Natural Science Foundation of China[Grant Number 11671142].

摘　　要：In the paper,the autoregressive moving average model for matrix time series(MARMA)is inves-tigated.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 suf-ficient conditions for stationarity and invertibility,model parameter estimation,model testing and model forecasting.

关 键 词：Matrix time series autoregressive moving average model bilinear model statistical inference 

分 类 号：O241.5[理学—计算数学]