TY - BOOK
AU - Bühlmann,Peter
AU - van de Geer,Sara
ED - SpringerLink (Online service)
TI - Statistics for High-Dimensional Data: Methods, Theory and Applications
T2 - Springer Series in Statistics,
SN - 9783642201929
AV - QA276-280
U1 - 519.5 23
PY - 2011///
CY - Berlin, Heidelberg
PB - Springer Berlin Heidelberg
KW - Statistics
KW - Mathematical statistics
KW - Statistical Theory and Methods
KW - Probability and Statistics in Computer Science
N1 - 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
N2 - 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
UR - http://dx.doi.org/10.1007/978-3-642-20192-9
ER -