Methods, Theory and Applications
Edición de la obra Statistics for High-Dimensional Data
| Autor | Peter Bühlmann |
|---|---|
| Editorial | Springer-Verlag Berlin Heidelberg |
| Fecha de publicación | 2011 |
| Lugar | Berlin, Heidelberg |
| Idioma | inglés |
| Páginas | 556 |
| Formato | [electronic resource] : |
| ISBN-13 | 9783642201912, 9783642201929 |
| OCLC | 729346867 |
| LCCN | 2011930793 |
| Serie | Springer Series in Statistics |
| Número de Cutter | B931s |
|---|
Modern statistics deals with large and complex data sets, and consequently with models containing a large number of parameters. This book presents a detailed account of recently developed approaches, including the Lasso and versions of it for various models, boosting methods, undirected graphical modeling, and procedures controlling false positive selections. A special characteristic of the book is that it contains comprehensive mathematical theory on high-dimensional statistics combined with methodology, algorithms and illustrations with real data examples. This in-depth approach highlights the methods’ great potential and practical applicability in a variety of settings. As such, it is a valuable resource for researchers, graduate students and experts in statistics, applied mathematics and computer science.