Introduction to High-Dimensional Statistics

The book includes revised chapters, three new chapters, and enhanced appendices on iterative algorithms, clustering, and minimax lower bounds.

\n Format: Hardback
\n Length: 346 pages
\n Publication date: 26 August 2021
\n Publisher: Taylor & Francis Ltd
\n

Here is the rephrased text:

The second edition of this popular textbook has been extensively revised and updated to cover the latest developments in the field of statistics. It includes revised chapters from the previous edition, with the inclusion of numerous additional materials on some important topics, such as 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.

In addition to the revised chapters, the book also features three new chapters on iterative algorithms, clustering, and minimax lower bounds. These chapters provide comprehensive coverage of these topics, which are essential in modern statistical analysis.

The appendices have been extensively enhanced, with a particular focus on minimax lower-bounds. The appendices now include the Davis-Kahan perturbation bound and two simple versions of Hanson-Wright concentration inequality, which are valuable tools for practitioners in the field.

This second edition of the textbook is an invaluable resource for students, researchers, and practitioners in the field of statistics. It provides a comprehensive and up-to-date introduction to the latest statistical techniques and applications, and is sure to be a valuable tool for years to come.

\n Weight: 700g\n
Dimension: 161 x 242 x 27 (mm)\n
ISBN-13: 9780367716226\n
Edition number: 2 New edition\n