Bühlmann, Peter; van de Geer, Sara 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. Cited in 3 ReviewsCited in 604 Documents MSC: 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 PDFBibTeX XMLCite \textit{P. Bühlmann} and \textit{S. van de Geer}, Statistics for high-dimensional data. Methods, theory and applications. Berlin: Springer (2011; Zbl 1273.62015) Full Text: DOI