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.
- 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
The book is available as a hardcover, softcover, and ebook. Most libraries provide free access to the ebook and printed copies—ask your library otherwise! You can get to the ebook through your library system or through SpringerLink.
Lederer, J., 2022. Fundamentals of High-Dimensional Statistics: With Exercises and R Labs. Springer Texts in Statistics.
title="Fundamentals of High-Dimensional Statistics: With Exercises and R Labs",
publisher="Springer Texts in Statistics",
Data for the R lab of Chapter 3: GraphicalModels_Lab_Data.rda