【专家简介】：朱复康，吉林大学数学学院教授、博士生导师。2008年博士毕业，2013年破格晋升教授。主要从事时间序列分析和金融统计的研究，已经在Journal of the American Statistical Association、Annals of Applied Statistics、Journal of Business & Economic Statistics、Statistica Sinica、Scandinavian Journal of Statistics、Journal of Time Series Analysis、中国科学-数学等期刊上发表论文多篇，主持国家自然科学基金面上项目4项和青年基金1项。曾获教育部自然科学奖二等奖、吉林省科学技术奖二等奖、Journal of Time Series Analysis期刊杰出作者，入选吉林省享受省政府津贴专家、长春市有突出贡献专家，连续四年(2023-2026)入选美国斯坦福大学发布的全球前2%顶尖科学家榜单。现任教育部统计学类专业教学指导委员会委员、吉林省现场统计研究会副理事长、中国现场统计研究会理事、全国工业统计学教学研究会常务理事，以及SCI期刊Statistical Papers、Journal of Statistical Computation and Simulation、Methodology and Computing in Applied Probability的副主编。指导的研究生1人获得吉林省优秀博士学位论文、3人获得吉林省优秀硕士学位论文。
【报告摘要】：Matrix-variate integer-valued time series analysis is of fundamental importance in econometrics. Numerous economic indicators take non-negative integer values with explicit economic meanings. The matrix structure preserves the multidimensional information inherent in the data and reveals the spatio-temporal interactions within economic systems, thereby facilitating the understanding of complex economic behaviors and structural changes. To analyze such data, this paper proposes a novel matrix-variate integer-valued autoregressive model. The key techniques lie in defining two left- and right-matrix thinning operators. The probabilistic and statistical properties of the proposed model are investigated. Furthermore, two estimation methods are developed: projection estimation and iterative least squares estimation. The corresponding asymptotic properties of these estimators are established. Additionally, the order-determination problem is addressed. To accommodate high-dimensional settings, two types of reduced-rank matrix-variate integer-valued autoregressive models are developed. In the simulation studies, the estimation results are given and the theoretical properties are verified. Finally, it is shown that the matrix-variate integer-valued autoregressive model is superior to the continuous matrix-variate autoregressive and multivariate integer-valued autoregressive models for two real examples.
【报告时间】：2026年11月2日（周一）10:00-11:00
【报告地点】：位育楼417
