Statistics for High-Dimensional Data
Subtitle: Methods, Theory and Applications
By: by Peter Bühlmann, Sara van de Geer
Contributors: van de Geer, Sara; SpringerLink (Online service)
Series: Springer Series in Statistics
Publisher: Springer-Verlag Berlin Heidelberg
Published: 2011
Place of publication: Berlin, Heidelberg
Language: eng
Format: [electronic resource] :
Number of pages: 556
ISBN-13: 9783642201912; 9783642201929
Subjects: Mathematical statistics; Statistics; Computer science
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.