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  • Textbook
  • © 2022

Fundamentals of High-Dimensional Statistics

With Exercises and R Labs

  • Introduces readers to the mathematical tools and principles of high-dimensional statistics

  • Includes numerous exercises, many of them with detailed solutions

  • Features computer labs in R that convey valuable practical insights

  • Offers suggestions for further reading

Part of the book series: Springer Texts in Statistics (STS)

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eBook USD 109.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-73792-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book USD 139.99
Price excludes VAT (USA)

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Table of contents (7 chapters)

  1. Front Matter

    Pages I-XIV
  2. Introduction

    • Johannes Lederer
    Pages 1-35
  3. Linear Regression

    • Johannes Lederer
    Pages 37-79
  4. Graphical Models

    • Johannes Lederer
    Pages 81-108
  5. Tuning-Parameter Calibration

    • Johannes Lederer
    Pages 109-137
  6. Inference

    • Johannes Lederer
    Pages 139-167
  7. Theory I: Prediction

    • Johannes Lederer
    Pages 169-210
  8. Theory II: Estimation and Support Recovery

    • Johannes Lederer
    Pages 211-237
  9. Back Matter

    Pages 239-355

About this book

This textbook provides a step-by-step introduction to the tools and principles of high-dimensional statistics. Each chapter is complemented by numerous exercises, many of them with detailed solutions, and computer labs in R that convey valuable practical insights. The book covers the theory and practice of high-dimensional linear regression, graphical models, and inference, ensuring readers have a smooth start in the field. It also offers suggestions for further reading. Given its scope, the textbook is intended for beginning graduate and advanced undergraduate students in statistics, biostatistics, and bioinformatics, though it will be equally useful to a broader audience.

Keywords

  • high-dimensional statistics
  • regularization
  • sparsity
  • linear regression
  • graphical models
  • high-dimensional data
  • R labs
  • prediction
  • estimation
  • lasso
  • calibration
  • statistical inference
  • exercises and solutions
  • high-dimensional inference

Authors and Affiliations

  • Statistics, Machine Learning & Data Science, Ruhr-University Bochum, Bochum, Germany

    Johannes Lederer

About the author

Johannes Lederer is a Professor of Statistics at the Ruhr-University Bochum, Germany. He received his PhD in mathematics from the ETH Z├╝rich and subsequently held positions at UC Berkeley, Cornell University, and the University of Washington. He has taught high-dimensional statistics to applied and mathematical audiences alike, e.g. as a Visiting Professor at the Institute of Statistics, Biostatistics, and Actuarial Sciences at UC Louvain, and at the University of Hong Kong Business School.

Bibliographic Information

Buying options

eBook USD 109.00
Price excludes VAT (USA)
  • ISBN: 978-3-030-73792-4
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Hardcover Book USD 139.99
Price excludes VAT (USA)