Fundamentals of high-dimensional statistics : with exercises and R Labs / Johannes Lederer

By:

Lederer, Johannes, 1984- [VerfasserIn]

Resource type: Ressourcentyp: Buch (Online)Book (Online)Language: English Series: Springer texts in statisticsPublisher: Cham : Springer, [2022]Description: 1 Online-Ressource (XIV, 355 Seiten) : Illustrationen, DiagrammeISBN:

9783030737924

Subject(s):

Additional physical formats: 9783030737917. | 9783030737948. | Erscheint auch als: 9783030737917 Druck-Ausgabe | Erscheint auch als: 9783030737931 Druck-Ausgabe | Erscheint auch als: 9783030737948 Druck-AusgabeDOI: DOI: 10.1007/978-3-030-73792-4Online resources:

Summary: 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. .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.PPN: PPN: 1778352693Package identifier: Produktsigel: ZDB-2-SMA | ZDB-2-SXMS | ZDB-2-SEB

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