Abstract:
Factor models have been widely applied in economics and finance, and the well-known heavy-tailedness of macroeconomic/financial data should be taken into account. In this talk, I will introduce the existing robust factor analysis methods, namely, the Huber Principal Component Analysis (HPCA), the Quantile Factor Analysis (QFA) and the Robust Two Step (RTS). In recent years, matrix-valued or even high order tensor time series have been common in areas of economics and finance. I will also introduce the existing robust factor analysis tools for well-structured matrix/tensor data, extending the HPCA, QFA and RTS in a proper way. The talk is based on some recent work by our group and we also develop an R package “HDRFA” which is available at my personal website https://heyongstat.github.io/.
About the Speaker:
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。何老师主要从事金融计量统计、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 (封面文章), Biostatistics、中国科学：数学等发表研究论文30余篇。此外，何老师还主持了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），并担任美国数学评论评论员，及JRSSB, JRSSC, Biometrics, EJS,JMVA等国际知名学术期刊匿名审稿人。
Your participation is warmly welcomed!
欢迎扫码关注北大统计科学中心公众号，了解更多讲座信息!
