Introduction to High-Dimensional Statistics, Second Edition preserves the philosophy of the first edition: to be a concise guide for students and researchers discovering the area and interested in the mathematics involved. The main concepts and ideas are presented in simple settings, avoiding thereby unessential technicalities. High-dimensional statistics is a fast-evolving field and much progress has been made on a large variety of topics, providing new insights and methods. Offering a succinct presentation of the mathematical foundations of high-dimensional statistics, this new edition features:

• Revised chapters from the previous edition, with the inclusion of many additional materials on some important topics, including compress sensing, estimation with convex constraints, the slope estimator, simultaneously low rank and row sparse linear regression, or aggregation of a continuous set of estimators.

• Three new chapters on iterative algorithms, clustering and minimax lower bounds.
• Enhanced appendices, minimax lower-bounds mainly with the addition of Davis-Kahan perturbation bound and of two simple versions of Hanson-Wright concentration inequality.
• Covers cutting-edge statistical methods including model selection, sparsity and the lasso, iterative hard thresholding, aggregation, support vector machines and learning theory
• Provides detailed exercises at the end of every chapter with collaborative solutions on a wikisite.
• Illustrates concepts with simple but clear practical examples.