Dewey Class |
519.5 (DDC 23) |
Title |
Statistics for High-Dimensional Data (EB) : Methods, Theory and Applications / by Peter Bühlmann, Sara van de Geer. |
Author |
Bühlmann, Peter |
Added Personal Name |
van de Geer, Sara author. |
Other name(s) |
SpringerLink (Online service) |
Publication |
Berlin, Heidelberg : Springer , 2011. |
Physical Details |
XVII, 556p. 31 illus., 8 illus. in color. : online resource. |
Series |
Springer Series in Statistics 0172-7397 |
ISBN |
9783642201929 |
Summary Note |
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 methodsâ 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.: |
Contents note |
Introduction -- Lasso for linear models -- Generalized linear models and the Lasso -- The group Lasso -- Additive models and many smooth univariate functions -- Theory for the Lasso -- Variable selection with the Lasso -- Theory for l1/l2-penalty procedures -- Non-convex loss functions and l1-regularization -- Stable solutions -- P-values for linear models and beyond -- Boosting and greedy algorithms -- Graphical modeling -- Probability and moment inequalities -- Author Index -- Index -- References -- Problems at the end of each chapter. |
System details note |
Online access is restricted to subscription insitutions through IP address (only for SISSA internal users) |
Internet Site |
http://dx.doi.org/10.1007/978-3-642-20192-9 |
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