Tony Cai, Dorothy Silberberg Professor of Statistics at the Wharton School of the University of Pennsylvania
Ming Yuan, Professor, Department of Statistics, University of Wisconsin-Madison
Time：14:00-17:40, July 8, July 10, July 12; 08:00-11:40, July 9, July 11, July 13 (Week 20)
Classroom：Room 601, Zhiyuan College, Pao Yue-Kong Library, SJTU
This course will cover high-dimensional statistical inference with the focus on the recovery of high dimensional sparse signals and the estimation of large matrices. These and other related problems have attracted much recent interest in a range of fields including statistics, applied mathematics and electrical engineering. We will discuss in detail the penalized and constrainedl1minimization methods and give a unified and elementary analysis on sparse signal recovery in three settings: noiseless, bounded noise and Gaussian noise. This course will also present the latest results on optimal estimation of large covariance/precision matrices. More specially, the course will cover the following topics:
Time permitting, high dimensional linear discriminant analysis will also be discussed at the end.