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(yuǎn)樓1709學(xué)術(shù)報告廳

主辦單位：數(shù)學(xué)研究院、數(shù)學(xué)與統(tǒng)計學(xué)院、科學(xué)技術(shù)研究院

報告人簡介：

      孔新兵，南京審計大學(xué)統(tǒng)計與數(shù)據(jù)科學(xué)學(xué)院教授，主要研究興趣為高頻、高維數(shù)據(jù)統(tǒng)計推斷與機(jī)器學(xué)習(xí)。主持國家自然科學(xué)基金3項，參與重點項目1項。在統(tǒng)計學(xué)頂級期刊和計量經(jīng)濟(jì)學(xué)頂級期刊發(fā)表論文22篇。獲第一屆統(tǒng)計科學(xué)技術(shù)進(jìn)步獎等獎項。擔(dān)任RMTA和《應(yīng)用概率統(tǒng)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.