Fundamentals of high-dimensional statistics : with exercises and R labs / Johannes Lederer.
2022
QA276
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Unlimited
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Can lend chapters, not whole ebooks
Details
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
3030737926 (electronic bk.)
3030737918
9783030737917
Publication Details
Cham, Switzerland : Springer, [2022]
Language
English
Description
1 online resource
Item Number
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.
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Online Access
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Table of Contents
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.
Notation
Introduction
Linear Regression
Graphical Models
Tuning-Parameter Calibration
Inference
Theory I: Prediction
Theory II: Estimation and Support Recovery
A Solutions
B Mathematical Background.