【专家简介】何勇，山东大学金融研究院，研究员，山东大学未来青年学者；山东大学学士（2012），复旦大学博士（2017），师从张新生教授；从事金融计量统计、生物统计以及机器学习等方面的研究，在国际计量及统计学权威期刊Journal of Econometrics, Journal of Business and Economic Statistics, Biometrics, Biostatistics, Statistics in Medicine, Journal of Multivariate Analysis、中国科学：数学等发表研究论文30余篇；主持国家自然科学基金面上项目、青年基金，全国统计科学研究重点项目等，获第一届统计科学技术进步奖（第二位）。担任美国数学评论评论员，及JRSSB, JRSSC, Biometrics, EJS, SINICA等十余个国际知名学术期刊匿名审稿人。
【报告摘要】In this article, we first propose generalized row/column matrix Kendall's tau for matrix-variate observations that are ubiquitous in areas such as finance and medical imaging. For a random matrix following a matrix-variate elliptically contoured distribution, we show that the eigenspaces of the proposed row/column matrix Kendall's tau coincide with those of the row/column scatter matrix respectively, with the same descending order of the eigenvalues. We perform eigenvalue decomposition to the generalized row/column matrix Kendall's tau for recovering the loading spaces of the matrix factor model. We also propose to estimate the pair of the factor numbers by exploiting the eigenvalue-ratios of the row/column matrix Kendall's tau. Theoretically, we derive the convergence rates of the estimators for loading spaces, factor scores and common components, and prove the consistency of the estimators for the factor numbers without any moment constraints on the idiosyncratic errors. Thorough simulation studies are conducted to show the higher degree of robustness of the proposed estimators over the existing ones. Analysis of a financial dataset of asset returns and a medical imaging dataset associated with COVID-19 illustrate the empirical usefulness of the proposed method. This is a joint work with Yalin Wang, Long Yu, Wang Zhou and Wen-Xin Zhou.
腾讯会议号：445-160-383
时 间：2022年11月16日 19:00-20:00