报 告 人： 孔新兵 博士
复旦大学管理学院统计系
报告题目：Testing of high dimensional mean vectors via approximate factor model
报告时间：2013年12月2日下午2:00
报告地点：静远楼1506学术报告厅
主办单位：太阳成集团、科技处
报告内容摘要：In high dimensional setting, testing of means usually requires imposing sparsity conditions on the population mean vector and/or the covariance matrix underlying the observed data. However, this is rarely true in many scenarios in social science, biology, etc., where the variables are possibly highly correlated due to existence of common factors. In this talk, we assume that the correlated variables are generated from the approximate factor model. We then correct the common factors from the original data and based on the factor-corrected data we redo the test of means invented in Cai et al. (2013). It turns out that, on one hand, the newly proposed testing procedure is more powerful than the CLX test based on the original data due to the increase of the signal to noise ratio, and on the other hand, we only need the sparsity condition on the covariance structure of the idiosyncratic error term which can be met more easily than that on the original data.
孔新兵博士简介：
复旦大学管理学院统计系讲师，研究兴趣为金融统计、高维数据分析、多重检验等。在统计学和计量经济学杂志发表论文多篇，其中包括顶级杂志 Journal of the American Statistical Association, Annals of Statistics, Journal of Econometrics， 以及权威杂志 PLoS ONE, Test, Scandinavion Journal of Statistic。被评为2012年度复旦管院学术新星（唯一获得者）。为SCI 杂志 Journal of Business and Economic Statistics, Quantitive Finance, Journal of Non-parametric Statistics, Science in China 匿名审稿人。