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商品詳細
Statistics for High-Dimensional Data : Methods, Theory and Applications. 高次元データのための統計学
・ISBN 978-3-642-20191-2 hard EUR 159.99
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| 著者・編者 | Bühlmann, Peter / van de Geer, S., |
|---|---|
| シリーズ | Springer Series in Statistics |
| 出版社 | (Springer, GW) |
| 出版年月 | 2011 |
| ページ数 | xviii, 556 S. |
| 言語 | ENG |
| ニュース番号 | ＜586-281＞ |
解説
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.