Statistics for High-Dimensional Data - Methods, Theory and Applications (Hardcover, 2011 Ed.)

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.

General

Imprint:	Springer-Verlag
Country of origin:	Germany
Series:	Springer Series in Statistics
Release date:	June 2011
First published:	2011
Authors:	Peter Buhlmann • Sara Van De Geer
Dimensions:	235 x 155 x 36mm (L x W x T)
Format:	Hardcover
Pages:	558
Edition:	2011 Ed.
ISBN-13:	978-3-642-20191-2
Categories:	Books > Science & Mathematics > Mathematics > Probability & statistics
LSN:	3-642-20191-1
Barcode:	9783642201912

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