Springer Series in Statistics Statistics for High-Dimensional Data: Methods, Theory and Applications
Type
Book
Authors
ISBN 10
3642201911
ISBN 13
9783642201912
Category
Unknown
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Publication Year
2011
Publisher
Pages
558
Series Name
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. - from Amzon
Number of Copies
1
