Buch, Englisch, 355 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 723 g
Reihe: Springer Texts in Statistics
With Exercises and R Labs
Buch, Englisch, 355 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 723 g
Reihe: Springer Texts in Statistics
ISBN: 978-3-030-73791-7
Verlag: Springer International Publishing
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.
Zielgruppe
Upper undergraduate
Autoren/Hrsg.
Fachgebiete
- Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Maschinelles Lernen
- Mathematik | Informatik EDV | Informatik Daten / Datenbanken Big Data
- Mathematik | Informatik Mathematik Stochastik Mathematische Statistik
- Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen
Weitere Infos & Material
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.