High Dimensional Statistics

Introduction to High Dimensional Statistics 🚀

Last updated on Oct 20, 2019 2 min read Topics

Prerequisites:

Courses:

Mathematics of High-Dimensional Data by Yuxin Chen - HDS
Optimization Methods for High-Dimensional Statistics by Anthony Man-Cho So - HDS Opt.
Information-theoretic methods in HDS by Yihong Wu - HDS

Lecture Notes:

An Introduction to Matrix Concentration Inequalities by Joel A. Tropp - Inequalities
Mathematics of sparsity by Emmanuel Cand`es - Sparsity
Introduction to the non-asymptotic analysis of random matrices by Roman Vershynin - Random Matrix
Statistical Machine Learning for High Dimensional Data by John Lafferty and Larry Wasserman - SLT for HD data
High Dimensional Statistics Lecture Notes by Philippe Rigollet and Jan-Christian H¨utter - HDS MIT
High Dimensional Statistics Lecture Notes by Martin J. Wainwright - HDS
High Dimensional Statistics by Peter Buhlmann - HDS Notes

Books:

Statistical Learning with Sparsity: The Lasso and Generalizations by Trevor Hastie, Robert Tibshirani and Martin Wainwright - SL with Sparsity
Topics in random matrix theory by Terence Tao - Random Matrix
Graphical Models, Exponential Families, and Variational Inference by Martin J. Wainwright and Michael I. Jordan - Graphical Models
High-dimensional probability: An introduction with applications in data science by Roman Vershynin - HD Probability
A Mathematical Introduction to Compressive Sensing by Simon Foucart Holger Rauhut - Compressive Sensing
A High-dimensional data analysis with sparse models: Theory, algorithms, and applications by John Wright, Yi Ma and Allen Yang - HD Sparse
High-dimensional statistics: A non-asymptotic viewpoint by Martin J. Wainwright - Non-asymtotic veiwpoint
Sparse and Redundant Representations by Elad, Michael - Sparse Representations

Blogs:

My research interests include large-scale optimization