High-Dimensional Statistics: A Non-Asymptotic Viewpoint

Martin J. Wainwright

High-Dimensional Statistics: A Non-Asymptotic Viewpoint is a comprehensive text that covers the foundations and recent developments in the field of high-dimensional statistics. The book is authored by Martin J. Wainwright, who is a professor of Electrical Engineering and Computer Science at the University of California, Berkeley. Wainwright is also a fellow of the Institute of Mathematical Statistics and a recipient of the ACM Infosys Foundation Award.

The book follows a non-asymptotic viewpoint, which means that it focuses on finite sample analysis rather than asymptotic analysis. The book starts with a review of basic probability theory and then moves on to cover topics such as high-dimensional inference, estimation, hypothesis testing, and regression. The book also discusses random matrix theory, optimization, convex analysis, and empirical processes.

One of the unique features of this book is its emphasis on the analysis of algorithms for high-dimensional statistics. The book covers a broad range of methods, including linear and non-linear methods, as well as supervised and unsupervised learning algorithms. The book also includes detailed discussions of several popular algorithms, such as the Lasso, Ridge regression, Principal Components Analysis (PCA), and Random Projections.

Overall, High-Dimensional Statistics: A Non-Asymptotic Viewpoint is a valuable resource for researchers and graduate students in the field of high-dimensional statistics. The book provides a comprehensive coverage of the foundations and recent developments in the field, and its focus on finite sample analysis makes it a useful reference for researchers working on practical problems.

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