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Statistics for High-Dimensional Data

Methods, Theory and Applications

  • Peter Bühlmann
  • Sara van de Geer
Book

Part of the Springer Series in Statistics book series (SSS)

Table of contents

  1. Front Matter
    Pages i-xvii
  2. Peter Bühlmann, Sara van de Geer
    Pages 1-6
  3. Peter Bühlmann, Sara van de Geer
    Pages 7-43
  4. Peter Bühlmann, Sara van de Geer
    Pages 45-53
  5. Peter Bühlmann, Sara van de Geer
    Pages 55-76
  6. Peter Bühlmann, Sara van de Geer
    Pages 77-97
  7. Peter Bühlmann, Sara van de Geer
    Pages 99-182
  8. Peter Bühlmann, Sara van de Geer
    Pages 183-247
  9. Peter Bühlmann, Sara van de Geer
    Pages 249-291
  10. Peter Bühlmann, Sara van de Geer
    Pages 293-338
  11. Peter Bühlmann, Sara van de Geer
    Pages 339-358
  12. Peter Bühlmann, Sara van de Geer
    Pages 359-386
  13. Peter Bühlmann, Sara van de Geer
    Pages 387-431
  14. Peter Bühlmann, Sara van de Geer
    Pages 433-480
  15. Peter Bühlmann, Sara van de Geer
    Pages 481-538
  16. Back Matter
    Pages 539-556

About this book

Introduction

Modern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book presents a detailed account of recently developed approaches, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections.
A special characteristic of the book is that it contains comprehensive mathematical theory on high-dimensional statistics combined with methodology, algorithms and illustrations with real data examples. This in-depth approach highlights the methods’ great potential and practical applicability in a variety of settings. As such, it is a valuable resource for researchers, graduate students and experts in statistics, applied mathematics and computer science.

Keywords

L1-regularization algorithms oracle inequalities sparsity variable and feature selection

Authors and affiliations

  • Peter Bühlmann
    • 1
  • Sara van de Geer
    • 2
  1. 1., Department of MathematicsSeminar for StatisticsZürichSwitzerland
  2. 2.ZürichSwitzerland

Bibliographic information

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