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.
Assessment
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.
Certification
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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 | ||
Assessment | |||
Submit Your Assignment | 00:00:00 | ||
Certification | 00:00:00 |
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