Statistics for High-Dimensional Data: Methods, Theory and Applications

Buhlmann, Peter, and Van de Geer, Sara

ISBN 10: 3642268579 / ISBN 13: 9783642268571

Published by Springer, 2013

About this Item

***INTERNATIONAL EDITION*** Read carefully before purchase: This book is the international edition in mint condition with the different ISBN and book cover design, the major content is printed in full English as same as the original North American edition. The book printed in black and white, generally send in twenty-four hours after the order confirmed. All shipments contain tracking numbers. Great professional textbook selling experience and expedite shipping service. Trade paperback (US). Glued binding. 558 p. Contains: Illustrations, color. Springer Series in Statistics. Audience: General/trade.

Seller Inventory # K709A0001333

Bibliographic Details

• Title: Statistics for High-Dimensional Data: Methods, Theory and Applications
• Publisher: Springer
• Publication Date: 2013
• Language: English
• ISBN 10: 3642268579
• ISBN 13: 9783642268571
• Binding: Soft cover
• Edition: International Edition
• Condition: New
• Dust Jacket Condition: New

About this title

Synopsis

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.

About the Author

Peter Bühlmann is Professor of Statistics at ETH Zürich. His main research areas are high-dimensional statistical inference, machine learning, graphical modeling, nonparametric methods, and statistical modeling in the life sciences. He is currently editor of the Annals of Statistics. He was awarded a Medallion lecture by the Institute of Mathematical Statistics in 2009 and read a paper to the Royal Statistical Society in 2010.

Sara van de Geer has been a full professor at the ETH in Zürich since 2005. Her main areas of research are empirical process theory, statistical learning theory, and nonparametric and high-dimensional statistics. She is an associate editor of Probability Theory and Related Fields, The Scandinavian Journal of Statistics and Statistical Surveys and a member of the Swiss National Science Foundation and correspondent of the Dutch Royal Academy of Sciences.
She received the IMS medal in 2003 and the ISI award in 2005, and was an invited speaker at the International Conference of Mathematicians in 2010.