Matrix-Variate Time Series Analysis: A Brief Review and Some New Developments

IF 1.7 3区数学 Q1 STATISTICS & PROBABILITY

International Statistical Review Pub Date : 2023-11-02 DOI:10.1111/insr.12558

Ruey S. Tsay

引用次数: 0

Abstract

This paper briefly reviews the recent research in matrix-variate time series analysis, discusses some new developments, especially for seasonal time series, and demonstrates some applications. A general matrix autoregressive moving-average model is introduced. The paper narrates a simple approach for understanding the model, identifiability issues, and estimation. Real examples are used to illustrate the theory.

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矩阵变量时间序列分析：简要回顾和一些新发展

本文简要回顾了矩阵变量时间序列分析的最新研究成果，讨论了一些新的发展，特别是季节性时间序列分析，并演示了一些应用。本文介绍了一般矩阵自回归移动平均模型。论文阐述了理解模型、可识别性问题和估计的简单方法。文中使用了实际例子来说明理论。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

International Statistical Review 数学-统计学与概率论

CiteScore

4.30

自引率

5.00%

发文量

审稿时长

>12 weeks

期刊介绍： International Statistical Review is the flagship journal of the International Statistical Institute (ISI) and of its family of Associations. It publishes papers of broad and general interest in statistics and probability. The term Review is to be interpreted broadly. The types of papers that are suitable for publication include (but are not limited to) the following: reviews/surveys of significant developments in theory, methodology, statistical computing and graphics, statistical education, and application areas; tutorials on important topics; expository papers on emerging areas of research or application; papers describing new developments and/or challenges in relevant areas; papers addressing foundational issues; papers on the history of statistics and probability; white papers on topics of importance to the profession or society; and historical assessment of seminal papers in the field and their impact.