TY  - GEN
AU  - Wainwright,Martin J.
TI  - High-Dimensional statistics: a non-asymptotic viewpoint
T2  - Cambridge Series in Statistical and Probabilistic Mathematics
SN  - 9781108498029
U1  - SA.01 23
PY  - 2019///
CY  - UK
PB  - CUP
KW  - Mathematical Statistics
KW  - Big Data
N1  - Includes bibliographical references and index; 1. Introduction -- 2. Basic tail and concentration bounds -- 3. Concentration of measure -- 4. Uniform laws of large numbers -- 5. Metric entropy and its uses -- 6. Random matrices and covariance estimation -- 7. Sparse linear models in high dimensions -- 8. Principal component analysis in high dimensions -- 9. Decomposability and restricted strong convexity -- 10. Matrix estimation with rank constraints -- 11. Graphical models for high-dimensional data -- 12. reproducing kernel Hilbert spaces -- 13. Nonparametric least squares -- 14. Localization and uniform laws -- 15. Minimax lower bounds
N2  - Recent years have witnessed an explosion in the volume and variety of data collected in all scientific disciplines and industrial settings. Such massive data sets present a number of challenges to researchers in statistics and machine learning. This book provides a self-contained introduction to the area of high-dimensional statistics, aimed at the first-year graduate level. It includes chapters that are focused on core methodology and theory - including tail bounds, concentration inequalities, uniform laws and empirical process, and random matrices - as well as chapters devoted to in-depth exploration of particular model classes - including sparse linear models, matrix models with rank constraints, graphical models, and various types of non-parametric models. With hundreds of worked examples and exercises, this text is intended both for courses and for self-study by graduate students and researchers in statistics, machine learning, and related fields who must understand, apply, and adapt modern statistical methods suited to large-scale data
ER  -