| 书目名称 | Statistics for High-Dimensional Data |
| 副标题 | Methods, Theory and |
| 编辑 | Peter Bühlmann,Sara van de Geer |
| 视频video | |
| 概述 | Contains the fundamentals of the recent research in a very timely area.Gives an overview of the area and adds many new insights.There is a unique mix of methodology, theory, algorithms and application |
| 丛书名称 | Springer Series in Statistics |
| 图书封面 |  |
| 描述 | .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.. |
| 出版日期 | Book 2011 |
| 关键词 | L1-regularization; algorithms; oracle inequalities; sparsity; variable and feature selection |
| 版次 | 1 |
| doi | https://doi.org/10.1007/978-3-642-20192-9 |
| isbn_softcover | 978-3-642-26857-1 |
| isbn_ebook | 978-3-642-20192-9Series ISSN 0172-7397 Series E-ISSN 2197-568X |
| issn_series | 0172-7397 |
| copyright | Springer-Verlag Berlin Heidelberg 2011 |
发表于 2025-3-21 16:48:39
