Course: STAT 41500
Title: High-Dimensional Statistics I
Instructor: Sagnik Nandy
Class Schedule: Sec 1: TR 11:00 AM–12:20 PM in Jones 303
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.