Statistics for High-Dimensional Data Methods, Theory and Applications /

• Κύριοι συγγραφείς: Bühlmann, Peter (Συγγραφέας), van de Geer, Sara (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg, 2011.
• Σειρά: Springer Series in Statistics,
• Θέματα: Statistics.; Mathematical statistics.; Statistical Theory and Methods.; Probability and Statistics in Computer Science.

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 mo...

Πίνακας περιεχομένων:

• 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.