报 告 人：孔新兵 教授
报告题目：Matrix Quantile Factor Model
报告时间：2023年7月18日（周二）上午9：30
报告地点：静远楼1709学术报告厅
主办单位：数学研究院、数学与统计学院、科学技术研究院
报告人简介：
孔新兵，南京审计大学统计与数据科学学院教授，主要研究兴趣为高频、高维数据统计推断与机器学习。主持国家自然科学基金3项，参与重点项目1项。在统计学顶级期刊和计量经济学顶级期刊发表论文22篇。获第一届统计科学技术进步奖等奖项。担任RMTA和《应用概率统计》编委。
报告摘要：
In this talk, I will introduce a matrix quantile factor model for matrix-valued data with a low-rank structure. We estimate the row and column factor spaces via minimizing the empirical check loss function over all panels. We show the estimates converge at rate $1/\min\{\sqrt{p_1p_2}, \sqrt{p_2T},$ $\sqrt{p_1T}\}$ in average Frobenius norm, where $p_1$, $p_2$ and $T$ are the row dimensionality, column dimensionality and length of the matrix sequence. This rate is faster than that of the quantile estimates via ``flattening the matrix model into a large vector model. Smoothed estimates are given and their central limit theorems are derived under some mild condition. We provide three consistent criteria to determine the pair of row and column factor numbers. Extensive simulation studies and an empirical study justify our theory.