- Full Description
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.
- Table of Contents
Table of Contents
- 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.
- convex loss functions and l1
- Stable solutions.
- values for linear models and beyond.
- Boosting and greedy algorithms.
- Graphical modeling.
- Probability and moment inequalities.
- Author Index.
- Problems at the end of each chapter.
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