Statistics for high-dimensional data. Methods, theory and applications. (English) Zbl 1273.62015

Springer Series in Statistics. Berlin: Springer (ISBN 978-3-642-20191-2/hbk). xvii, 556 p. (2011).
Publisher’s 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.


62-02 Research exposition (monographs, survey articles) pertaining to statistics
62H12 Estimation in multivariate analysis
62Fxx Parametric inference
62Jxx Linear inference, regression
62Pxx Applications of statistics
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