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: Model Averaging
  name:
    name part: Fletcher, David.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2018
    edition: 1st ed. 2018.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: X, 107 p. 4 illus. online resource.
  abstract: This book provides a concise and accessible overview of model averaging, with a focus on applications. Model averaging is a common means of allowing for model uncertainty when analysing data, and has been used in a wide range of application areas, such as ecology, econometrics, meteorology and pharmacology. The book presents an overview of the methods developed in this area, illustrating many of them with examples from the life sciences involving real-world data. It also includes an extensive list of references and suggestions for further research. Further, it clearly demonstrates the links between the methods developed in statistics, econometrics and machine learning, as well as the connection between the Bayesian and frequentist approaches to model averaging. The book appeals to statisticians and scientists interested in what methods are available, how they differ and what is known about their properties. It is assumed that readers are familiar with the basic concepts of statistical theory and modelling, including probability, likelihood and generalized linear models.
  table of contents: Why Model Averaging? -- Bayesian Model Averaging -- Frequentist Model Averaging -- Summary and Future Directions.
  note: by David Fletcher.
  subject:
    topic: Statistics
  subject:
    topic: Ecology
  subject:
    topic: Biostatistics
  subject:
    topic: Statistical Theory and Methods
  subject:
    topic: Theoretical Ecology/Statistics
  subject:
    topic: Biostatistics
  subject:
    topic: Statistics for Business, Management, Economics, Finance, Insurance
  subject:
    topic: Statistics for Life Sciences, Medicine, Health Sciences
  classification: QA276-280
  classification: 519.5
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: SpringerBriefs in Statistics
  identifier: 9783662585412
  identifier: https://doi.org/10.1007/978-3-662-58541-2
  record info:
    record creation date: 190117
    record change date: 20191025221043.0
    record identifier: 978-3-662-58541-2