TY - JOUR
T1 - Autoregressive moving average model for matrix time series
AU - Wu, Shujin
AU - Bi, Ping
N1 - Publisher Copyright:
© 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Matrix time series
KW - autoregressive moving average model
KW - bilinear model
KW - statistical inference
UR - https://www.scopus.com/pages/publications/85173510826
U2 - 10.1080/24754269.2023.2262360
DO - 10.1080/24754269.2023.2262360
M3 - 文章
AN - SCOPUS:85173510826
SN - 2475-4269
VL - 7
SP - 318
EP - 335
JO - Statistical Theory and Related Fields
JF - Statistical Theory and Related Fields
IS - 4
ER -