Course: STAT 41500
Title: High-Dimensional Statistics I
Instructor(s): Chao Gao
Teaching Assistant(s): TBA
Class Schedule: Sec 01: TR 9:30 AM–10:50 AM in Ryerson 358
Textbook(s): None
Description: In this course, we will consider statistical estimation with a large number of parameters. Sometimes the number of parameters may exceed the sample size. Problems such as sparse linear regression, bandable covariance matrix estimation, Gaussian graphical model, sparse PCA and CCA, and isotonic regression will be covered. Along with these specific problems, we will also cover techniques including concentration of measure, convex optimization, approximate message passing, debiasing, and statistical-computational tradeoff.
Prerequisite(s): STAT 30100 and STAT 30400 and STAT 31015, or consent of instructor
