Advanced Statistical Theory II (450-2-20)
Instructors
Matey Neykov
Meeting Info
Parkes Hall 213: Mon, Wed 12:30PM - 1:50PM
Overview of class
This class is a continuation of STAT 450-1 Advanced Statistical Theory 1. The first half of the class will be devoted to minimax lower bounds for estimation and testing. These bounds include strategies based on Le Cam's approach, Fano's inequality, Yang and Barron's construction and Assouad's inequality. Additional topics covered by the course include: Uniform Law of Large Numbers, VC theory, matrix concentration inequalities. We will further read and present the results of some selected papers in high-dimensional statistics.
Registration Requirements
The prerequisites include linear algebra, real analysis, a statistics theory class (e.g. Casella and Berger level) and Advanced Statistical Theory 1
Learning Objectives
The student will be able to read and understand modern papers in high-dimensional statistics; The student will also be equipped with tools to do their own research in the area.
Teaching Method
lectures
Evaluation Method
There will be 1-2 homework assignments + project (which will be writing a report on a selected by the student paper in high-dimensional/nonparametric statistics) and a class presentation
Class Materials (Required)
Main refernece: High-dimensional Statistics: A non-asymptotic viewpoint by Martin Wainwright
Other references: John Ducci's lecture notes (https://web.stanford.edu/class/stats311/lecture-notes.pdf)
High-dimensional probability by Roman Vershynin
Introduction to nonparametirc estimation by Alexandre Tsybakov
Class Notes
This is a must take for anyone who wants to do research in the area of high-dimensional statistics.
Enrollment Requirements
Enrollment Requirements: Prerequisite: STAT 450-1 Advanced Statistical Theory 1
Add Consent: Department Consent Required