Overview
- 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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Table of contents (7 chapters)
Keywords
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.
Authors and Affiliations
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 Zürich 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
Book Title: Fundamentals of High-Dimensional Statistics
Book Subtitle: With Exercises and R Labs
Authors: Johannes Lederer
Series Title: Springer Texts in Statistics
DOI: https://doi.org/10.1007/978-3-030-73792-4
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Editor(s) (if applicable) and The Author(s), under exclusive licence to Springer Nature Switzerland AG 2022
Hardcover ISBN: 978-3-030-73791-7Published: 17 November 2021
Softcover ISBN: 978-3-030-73794-8Published: 18 November 2022
eBook ISBN: 978-3-030-73792-4Published: 16 November 2021
Series ISSN: 1431-875X
Series E-ISSN: 2197-4136
Edition Number: 1
Number of Pages: XIV, 355
Number of Illustrations: 13 b/w illustrations, 21 illustrations in colour
Topics: Statistical Theory and Methods, Big Data, Data Structures and Information Theory, Artificial Intelligence, Statistics and Computing/Statistics Programs, Machine Learning