報 告 人：孔新兵 教授

報告題目：Matrix Quantile Factor Model

報告時間：2023年7月18日（周二）上午9：30 

報告地點：靜遠樓1709學術報告廳

主辦單位：數學研究院、數學與統計學院、科學技術研究院

報告人簡介：

      孔新兵，南京審計大學統計與數據科學學院教授，主要研究興趣為高頻、高維數據統計推斷與機器學習。主持國家自然科學基金3項，參與重點項目1項。在統計學頂級期刊和計量經濟學頂級期刊發表論文22篇。獲第一屆統計科學技術進步獎等獎項。擔任RMTA和《應用概率統計》編委。

報告摘要：

      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.