2nd Edition

Introduction to High-Dimensional Statistics



  • Available for pre-order. Item will ship after July 15, 2021
ISBN 9780367716226
July 15, 2021 Forthcoming by Chapman and Hall/CRC
368 Pages 28 B/W Illustrations

USD $89.95

Prices & shipping based on shipping country


Preview

Book Description

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.

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.

...
View More

Author(s)

Biography

Christophe Giraud was a student of the École Normale Supérieure de Paris, and he received a Ph.D in probability theory from the University Paris 6. He was assistant professor at the University of Nice from 2002 to 2008. He has been associate professor at the École Polytechnique since 2008 and professor at Paris Sud University (Orsay) since 2012. His current research focuses mainly on the statistical theory of high-dimensional data analysis and its applications to life sciences.