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The data whose dimension is larger than dimensions considered in the classical multivariate analysis is studied in the field of High-Dimensional Statistics relying on the theory of random vectors. This [course_title] helps you solve the problems related to various proof techniques for state-of-the-art methods in regression, matrix estimation, and principal component analysis and optimally guarantees more effectively.  


This course does not involve any written exams. Students need to answer 5 assignment questions to complete the course, the answers will be in the form of written work in pdf or word. Students can write the answers in their own time. Each answer needs to be 200 words (1 Page). Once the answers are submitted, the tutor will check and assess the work.


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Course Credit: MIT

Course Curriculum

Lecture Notes 00:20:00
Introduction to High-Dimensional Statistics 00:15:00
Linear Regression Model 00:25:00
Matrix Estimation 00:25:00
Minimax Lower Bounds 00:20:00
Misspecified Linear Models 00:20:00
Sub-Gaussian Random Variables 00:30:00
Submit Your Assignment 00:00:00
Certification 00:00:00

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