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.
| Autorius: | Johannes Lederer |
| Serija: | Springer Texts in Statistics |
| Leidėjas: | Springer Nature Switzerland |
| Išleidimo metai: | 2022 |
| Knygos puslapių skaičius: | 372 |
| ISBN-10: | 3030737942 |
| ISBN-13: | 9783030737948 |
| Formatas: | Knyga minkštu viršeliu |
| Kalba: | Anglų |
| Žanras: | Machine learning |
Parašykite atsiliepimą apie „Fundamentals of High-Dimensional Statistics: With Exercises and R Labs“
