Summary

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.

Highlights

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

Available since

November 2021.

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Follow this link to get a hardcopy at the best price directly through Springer, and follow the same link to get the PDF through the library system of your university.

Cite

Lederer, J., 2022. Fundamentals of High-Dimensional Statistics: With Exercises and R Labs. Springer Texts in Statistics.

@book{2022Lederer, author="Lederer, Johannes", title="Fundamentals of High-Dimensional Statistics: With Exercises and R Labs", year="2022", publisher="Springer Texts in Statistics", pages="1--355", isbn="978-3-030-73792-4", url="https://doi.org/10.1007/978-3-030-73792-4_1"}