报 告 人：孔新兵 教授（南京审计大学）
报告时间： 2023年11月24日（星期五）10:00-11:00
报告地点： 海山楼A1101
校内联系人：牛一 副教授 联系电话：84708351-8081
报告摘要: In this talk, we give a projection estimation method for large-dimensional matrix factor models with cross-sectionally spiked eigenvalues. By projecting the observation matrix onto the row or column factor space, we simplify factor analysis for matrix series to that of a lower-dimensional tensor. This method also reduces the magnitudes of the idiosyncratic error components, thereby increasing the signal-to-noise ratio, because the projection matrix linearly filters the idiosyncratic error matrix. We theoretically prove that the projected estimators of the factor loading matrices achieve faster convergence rates than existing estimators under similar conditions. Asymptotic distributions of the projected estimators are also presented. A novel iterative procedure is given to specify the pair of row and column factor numbers. Extensive numerical studies verify the empirical performance of the projection method. Two real examples in finance and macroeconomics reveal factor patterns across rows and columns, which coincide with financial, economic, or geographical interpretations.
报告人简介：孔新兵，现为南京审计大学教授，博士生导师，院长。主要研究兴趣为高频与高维数据统计推断与机器学习；在统计学顶刊AoS, JASA, Biometrika, JoE, JBES发表论文19篇，独立作者3篇；主持国家自然科学基金项目3项，参与国家自然科学基金重点项目1项；现为国际统计学会选举会员，国际数理统计学会会员，中国现场统计研究会多元分析应用专业委员会副理事长，江苏省应用统计学会副理事长；获第一届统计科学技术进步奖一等奖；在全国概率统计会议等做大会报告；入选国家高层次青年人才计划。
