Statistics for High-Dimensional Data: Methods, Theory and ApplicationsModern 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. |
Contents
1 | |
7 | |
Chapter 3 Generalized linear models and the Lasso | 45 |
Chapter 4 The group Lasso | 54 |
Chapter 5 Additive models and many smooth univariate functions | 77 |
Chapter 6 Theory for the Lasso | 99 |
Chapter 7 Variable selection with the Lasso | 183 |
Chapter 8 Theory for l1 l2penalty procedures | 248 |
Chapter 10 Stable solutions | 339 |
Chapter 11 Pvalues for linear models and beyond | 359 |
Chapter 12 Boosting and greedy algorithms | 387 |
Chapter 13 Graphical modeling | 432 |
Chapter 14 Probability and moment inequalities | 481 |
539 | |
543 | |
References | 547 |
Other editions - View all
Statistics for High-Dimensional Data: Methods, Theory and Applications Peter Bühlmann,Sara van de Geer No preview available - 2013 |
Statistics for High-Dimensional Data: Methods, Theory and Applications Peter Bühlmann,Sara van de Geer No preview available - 2011 |
Statistics for High-Dimensional Data: Methods, Theory and Applications Peter Bühlmann,Sara van de Geer No preview available - 2011 |