18.S997 | Spring 2015 | Graduate
High-Dimensional Statistics
Course Description
This course offers an introduction to the finite sample analysis of high- dimensional statistical methods. The goal is to present various proof techniques for state-of-the-art methods in regression, matrix estimation and principal component analysis (PCA) as well as optimality guarantees. The course ends with research …

This course offers an introduction to the finite sample analysis of high- dimensional statistical methods. The goal is to present various proof techniques for state-of-the-art methods in regression, matrix estimation and principal component analysis (PCA) as well as optimality guarantees. The course ends with research questions that are currently open.

You can read more about Prof. Rigollet’s work and courses on his website

Learning Resource Types
assignment Problem Sets
notes Lecture Notes
A branched-tree diagram.
A map showing the genetic distance between individuals from the five major geographic regions of the globe determined using statistical analysis. (Courtesy of Nievergelt et al.)