Statistics for High-Dimensional Data

Description

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.

Subjects

Series Statement

• Springer Series in Statistics

Links

• http://dx.doi.org/10.1007/978-3-642-20192-9

Other Editions

• Statistics for High-Dimensional Data: Methods, Theory and Applications[electronic resource] :Springer-Verlag Berlin Heidelberg2011-01-01
• Statistics for High-Dimensional DataPaperbackSpringer2013-08-03