统计与管理学院2016年学术报告第9期
【主 题】Testing Super-Diagonal Structure in High-Dimensional Covariance Matrices
【报告人】 何婧
北京大学
【时 间】 2016年4月12日（星期二）13:30－14:30
【地 点】 上海财经大学统计与管理学院大楼1208室
【摘 要】 The covariance matrix of a random vector or a multivariate estimating function is a basic ingredient in multivariate analysis and econometrics in gaining information on the dependence between the components of the random vectors and the estimating functions.
When the dimension is comparable to or much larger than the sample size, the conventional inference methods are no longer applicable. See John (1972), Nagao (1973), Anderson (2003), etc.
We intend to propose a more powerful test than the existing tests which target on global features of.
【邀请人】 冯兴东
