Introduction to high-dimensional statistics
Author(s)
Bibliographic Information
Introduction to high-dimensional statistics
（Monographs on statistics and applied probability, 168）
CRC Press, Taylor & Francis Group, 2022
2nd ed.
- :hbk.
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Note
Includes bibliographical references and index
Description and Table of Contents
Description
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.
Table of Contents
1. Introduction. 2. Model Selection. 3. Minimax Lower Bounds. 4. Aggregation of Estimators. 5. Convex Criteria. 6. Iterative Algorithms. 7. Estimator Selection. 8. Multivariate Regression. 9. Graphical Models. 10. Multiple Testing. 11. Supervised Classification. 12. Clustering.
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