Introduction to High-Dimensional Statistics

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.