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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Detalles Bibliográficos
Clasificación:Libro Electrónico
Autores principales: Bühlmann, Peter (Autor), van de Geer, Sara (Autor)
Autor Corporativo: SpringerLink (Online service)
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2011.
Edición:1st ed. 2011.
Colección:Springer Series in Statistics,
Temas:
Statistics .
Computer science-Mathematics.
Mathematical statistics.
Statistical Theory and Methods.
Probability and Statistics in Computer Science.
Acceso en línea:Texto Completo
Tabla de Contenidos:
  • 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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