18.S997: High Dimensional Statistics

@inproceedings{Rigollet201518S997HD,
  title={18.S997: High Dimensional Statistics},
  author={Philippe Rigollet},
  year={2015}
}
Preface These lecture notes were written for the course 18.S997: High Dimensional Statistics at MIT. They build on a set of notes that was prepared at Princeton University in 2013-14. Over the past decade, statistics have undergone drastic changes with the development of high-dimensional statistical inference. Indeed, on each individual , more and more features are measured to a point that it usually far exceeds the number of observations. This is the case in biology and specifically genetics… CONTINUE READING

Citations

Publications citing this paper.
SHOWING 1-10 OF 26 CITATIONS

Fully Homomorphic Encryption with k-bit Arithmetic Operations

  • IACR Cryptology ePrint Archive
  • 2019
VIEW 3 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Covariance Matrix Estimation From Linearly-Correlated Gaussian Samples

  • IEEE Transactions on Signal Processing
  • 2019
VIEW 1 EXCERPT
CITES BACKGROUND

Concentration Bounds for Single Parameter Adaptive Control

  • 2018 Annual American Control Conference (ACC)
  • 2018