Fundamentals of High-Dimensional Statistics : With Exercises and R Labs /

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 prac...

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Bibliographic Details
Main Author:	Lederer, Johannes (Author)
Corporate Author:	SpringerLink (Online service)
Format:	eBook
Language:	English
Published:	Cham : Springer International Publishing : Imprint: Springer, 2022.
Edition:	1st ed. 2022.
Series:	Springer Texts in Statistics,
Subjects:	Statistics . Big data. Data structures (Computer science). Artificial intelligence. Machine learning. Statistical Theory and Methods. Big Data. Data Structures and Information Theory. Artificial Intelligence. Statistics and Computing/Statistics Programs. Machine Learning. Electronic books.
Online Access:	Connect to the full text of this electronic book

Description
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.
Physical Description:	1 online resource (XIV, 355 pages 34 illustrations, 21 illustrations in color.)
ISBN:	9783030737924
ISSN:	2197-4136
DOI:	10.1007/978-3-030-73792-4