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High-Dimensional Statistics
OCW Master Course Number
18.S997
Spring 2015
OCW_LOMv1.0
Author
Rigollet, Philippe
2020-12-28
OCW Course Topics
Mathematics
Probability and Statistics
OCW Course Topics
Mathematics
Discrete Mathematics
contents/index.htm.xml
High-Dimensional Statistics
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High-Dimensional Statistics
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18-s997s15-th.jpg
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18-s997s15.jpg
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Lecture Notes
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Chapter 4
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Chapter 2
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Chapter 3
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Chapter 5
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Introduction
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Chapter 1
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Complete Lecture Notes
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Assignments
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Problem Set 1
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Problem Set 2
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Syllabus
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Legal Notices
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Privacy Statement
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Trademark Notices
contents/18-s997s15-th.jpg.xml
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contents/lecture-notes/MIT18_S997S15_Chapter3.pdf.xml
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PK VQ~3! 18-s997-spring-2015/ReadMe.txtThis zip package contains the HTML pages and files associated with the course.
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MIT OpenCourseWare | Welcome
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PK p'QD* * 2 18-s997-spring-2015/contents/18-s997s15-th.jpg.xml
18-s997s15-th.jpg
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.
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LOMv1.0
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Spring 2015
OCW_LOMv1.0
Author
Rigollet, Philippe
2020-12-28
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LOMv1.0
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OCW Master Course Number
18.S997 High-Dimensional Statistics Spring 2015
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
CIP
270502
High Dimensional Statistics
Random Variables
Linear Regression
Misspecified Linear Models
Matrix Estimation
Minmax Lower Bounds
Sub-Gaussian
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