UID:
almahu_9949226753802882
Umfang:
XIV, 355 p. 34 illus., 21 illus. in color.
,
online resource.
Ausgabe:
1st ed. 2022.
ISBN:
9783030737924
Serie:
Springer Texts in Statistics,
Inhalt:
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.
Anmerkung:
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. .
In:
Springer Nature eBook
Weitere Ausg.:
Printed edition: ISBN 9783030737917
Weitere Ausg.:
Printed edition: ISBN 9783030737931
Weitere Ausg.:
Printed edition: ISBN 9783030737948
Sprache:
Englisch
DOI:
10.1007/978-3-030-73792-4
URL:
https://doi.org/10.1007/978-3-030-73792-4
