Fundamentals of high-dimensional statistics : with exercises and R labs / Johannes Lederer.
Lederer, Johannes C.
2022
QA276
Available Online

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Title Fundamentals of high-dimensional statistics : with exercises and R labs / Johannes Lederer.
Author Lederer, Johannes C.
ISBN 9783030737924 (electronic bk.)
3030737926 (electronic bk.)
3030737918
9783030737917
DOI https://doi.org/10.1007/978-3-030-73792-4
Imprint Cham, Switzerland : Springer, [2022]
Language English
Description 1 online resource
Other Standard Identifiers 10.1007/978-3-030-73792-4 doi
Call Number QA276
Dewey Decimal Classification 519.5
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.
Bibliography, etc. Note Includes bibliographical references and index.
Access Note Access limited to authorized users.
Digital File Characteristics text file
PDF
Source of Description Online resource; title from PDF title page (Springerlink, viewed November 29, 2021).
Series Springer texts in statistics, 2197-4136
Available in Other Form Fundamentals of high-dimensional statistics.
Linked Resources Online Access
Record Appears in Online Resources > Ebooks
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Preface
Notation
Introduction
Linear Regression
Graphical Models
Tuning-Parameter Calibration
Inference
Theory I: Prediction
Theory II: Estimation and Support Recovery
A Solutions
B Mathematical Background.

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