Fundamentals of High-Dimensional Statistics, 1st ed. 2022 With Exercises and R Labs Springer Texts in Statistics Series

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.

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.

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