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

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...

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Bibliographic Details
Call Number:Libro Electrónico
Main Authors: Bühlmann, Peter (Author), van de Geer, Sara (Author)
Corporate Author: SpringerLink (Online service)
Format: Electronic eBook
Language:Inglés
Published: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2011.
Edition:1st ed. 2011.
Series:Springer Series in Statistics,
Subjects:
Statistics .
Computer science-Mathematics.
Mathematical statistics.
Statistical Theory and Methods.
Probability and Statistics in Computer Science.
Online Access:Texto Completo
Table of Contents:
  • 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.

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