报告人：孔新兵 (南京审计大学)

时间：2021年7月8日15:00开始

地点：数统学院LD202

摘要：Quantile is an important measure in risk control in finance and quality assessment in service industry. In this paper, we model the temporal and cross-sectional interactive effect of the quantiles of a large-dimensional time series by a latent quantile factor model. The factor loadings and scores are learnt with statistical guarantee via an iterative check-loss-minimization procedure. Without any moment constraint on the idiosyncratic errors, we correctly identify the common and idiosyncratic components for each variable. We obtained the statistical convergence rates of the minimization estimators. Bahardur representations for the estimated factor loadings and scores are provided under some mild conditions. Moreover, a robust method is proposed to select the number of factors consistently. Simulation experiments checked the validity of the theory. Our analysis on a financial data set shows the superiority of the proposed method over other state-of-the-art methods.

简介:孔新兵，南京审计大学统计与数据科学学院教授、经理；国际统计协会(ISI)当选会员；获2012年度香港数学会“最佳博士论文奖”；主要研究兴趣为高频数据分析、髙维因子分析和经济金融计量分析；担任Random Matrices-Theory and Application和《应用概率统计》编委；中国现场统计研究会多个分会常务理事；在统计学顶级期刊The Annals of Statistics(AoS)、 Journal of the American Statistical Association(JASA)、Biometrika以及计量经济顶级期刊Journal of Econometrics(JoE)上发表论文16篇，独立发表AoS、Biometrika论文 3篇；主持国家基金项目4项，入选国家高层次青年人才计划。

邀请人：夏小超

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