rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Statistics for High-Dimensional Data
    sub title: Methods, Theory and Applications
  name:
    name part: Bühlmann, Peter.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: van de Geer, Sara.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2011
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVIII, 558 p. online resource.
  abstract: 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: 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.
  note: by Peter Bühlmann, Sara van de Geer.
  subject:
    topic: Statistics
  subject:
    topic: Mathematical statistics
  subject:
    topic: Statistics
  subject:
    topic: Statistical Theory and Methods
  subject:
    topic: Probability and Statistics in Computer Science
  classification: QA276-280
  classification: 519.5
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Springer Series in Statistics
  identifier: 9783642201929
  identifier: http://dx.doi.org/10.1007/978-3-642-20192-9
  record info:
    record creation date: 110719
    record change date: 20151125221126.0
    record identifier: 978-3-642-20192-9