Peter Bühlmann

Sara van de Geer

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.

Methods, Theory and Applications

Statistics for High-Dimensional Data

Marten Wegkamp

With this collections volume, some of the important works of Willem van Zwet are moved to the front layers of modern statistics. The selection was based on discussions with Willem, and aims at a representative sample. The result is a collection of papers that the new generations of statisticians should not be denied. They are here to stay, to enjoy and to form the basis for further research. The papers are grouped into six themes: fundamental statistics, asymptotic theory, second-order approximations, resampling, applications, and probability. This volume serves as basic reference for fundamental statistical theory, and at the same time reveals some of its history. The papers are grouped into six themes: fundamental statistics, asymptotic theory, second-order approximations, resampling, applications, and probability. This volume serves as basic reference for fundamental statistical theory, and at the same time reveals some of its history.

Sara van de Geer

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