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Fundamentals of high-dimensional statisticswith exercises and R labs /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Fundamentals of high-dimensional statisticsby Johannes Lederer.
Reminder of title:	with exercises and R labs /
Author:	Lederer, Johannes.
Published:	Cham :Springer International Publishing :2022.
Description:	xiv, 355 p. :ill., digital ;24 cm.
Contained By:	Springer Nature eBook
Subject:	Mathematical statistics.
Online resource:	https://doi.org/10.1007/978-3-030-73792-4
ISBN:	9783030737924$q(electronic bk.)

Fundamentals of high-dimensional statisticswith exercises and R labs /
Lederer, Johannes.

Fundamentals of high-dimensional statisticswith exercises and R labs /[electronic resource] :by Johannes Lederer. - Cham :Springer International Publishing :2022. - xiv, 355 p. :ill., digital ;24 cm. - Springer texts in statistics,2197-4136. - Springer texts in statistics..

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 -- Bibliography -- Index.

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.

ISBN: 9783030737924$q(electronic bk.)

Standard No.: 10.1007/978-3-030-73792-4doiSubjects--Topical Terms:

181877
Mathematical statistics.

LC Class. No.: QA276

Dewey Class. No.: 519.5

Fundamentals of high-dimensional statisticswith exercises and R labs /
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505 0 $a 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 -- Bibliography -- Index.
520 $a 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.
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