【专家简介】：高照省，电子科技大学数学科学学院、经济管理学院及人文社科高等研究院特聘教授、博士生导师。长期从事高维统计、时间序列分析与计量经济学研究。主持国家自然科学基金青年科学基金项目（B类，原优秀青年科学基金）、面上项目及青年科学基金项目（C类）等科研项目，入选四川省“天府峨眉计划”。研究成果发表于 Journal of the American Statistical Association、Journal of Econometrics、Journal of Business & Economic Statistics、IEEE Transactions on Information Theory 等国际学术期刊。现任统计和计量经济学权威期刊 Journal of Business & Economic Statistics 和 Journal of Forecasting 编委。
【报告摘要】：We develop a supervised factor-augmented matrix regression framework for high-dimensional analysis with weak factors. When factor loadings are concentrated on unknown subsets of rows and columns, conventional full-matrix factor extraction may suffer substantial signal dilution, with weakness in one matrix dimension propagating to estimation in the other. We characterize this two-way weak-loading phenomenon for both \(\alpha\)-PCA and projected estimators and show that their loading and factor estimates may deteriorate under weak signals. To address this problem, we propose a sequential response-guided screening procedure that identifies informative rows and columns prior to factor extraction. We establish exact support recovery and show that post-screening factor re-estimation converts the weak-loading problem into a pervasive one relative to the retained dimensions, yielding sharper convergence rates under substantially weaker dimensional growth conditions and consistent factor-number determination. We further establish asymptotic distributions for both unrestricted and bilinear regression estimators based on the estimated matrix factors. Simulations demonstrate substantial improvements in support recovery and factor estimation under weak signals. We illustrate the gains obtained by the response-guided factor construction with two empirical applications.
【报告时间】：2026年11月06日（周五）10:00-11:00
【报告地点】：位育楼417
