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18.S997 High-Dimensional Statistics | Mathematics

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... Show More

Keywords:: Linear Regression, Misspecified Linear Models, Minmax Lower Bounds, Sub-Gaussian, Probability and Statistics, Matrix Estimation, High Dimensional Statistics, Random Variables, Discrete Mathematics

Disciplines:

Mathematics and Statistics / Mathematics

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Material Type:: Online Course
Date Added to MERLOT:: May 12, 2016
Date Modified in MERLOT:: May 16, 2023
Author:: Unknown (Know the author?)
Submitter:: Barbra Bied Sperling

Primary Audience:: Graduate School
Technical Format:: Website
Mobile Compatibility:: Not specified at this time
Language:: English
Cost Involved:: No
Source Code Available:: No
Creative Commons:: This work is licensed under a Attribution-NonCommercial-ShareAlike 3.0 United States