Statistics for High-Dimensional Data
Bühlmann, Peter.
Statistics for High-Dimensional Data Methods, Theory and Applications / [recurso electrónico] : by Peter Bühlmann, Sara van de Geer. - XVIII, 558 p. online resource. - Springer Series in Statistics, 0172-7397 . - Springer Series in Statistics, .
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.
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.
9783642201929
Statistics.
Computer science.
Mathematical statistics.
Statistics.
Statistical Theory and Methods.
Probability and Statistics in Computer Science.
QA276-280
519.5
Statistics for High-Dimensional Data Methods, Theory and Applications / [recurso electrónico] : by Peter Bühlmann, Sara van de Geer. - XVIII, 558 p. online resource. - Springer Series in Statistics, 0172-7397 . - Springer Series in Statistics, .
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.
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.
9783642201929
Statistics.
Computer science.
Mathematical statistics.
Statistics.
Statistical Theory and Methods.
Probability and Statistics in Computer Science.
QA276-280
519.5
