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: Nonlinear Time Series: Nonparametric and Parametric Methods
  name:
    name part: Fan, Jianqing.
    role:
      role term: editor.
  name:
    name part: Yao, Qiwei.
    role:
      role term: editor.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: xxu
    date issued: 2003
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XIX, 553 p. 2 illus. online resource.
  abstract: Amongmanyexcitingdevelopmentsinstatisticsoverthelasttwodecades, nonlineartimeseriesanddata-analyticnonparametricmethodshavegreatly advanced along seemingly unrelated paths. In spite of the fact that the - plication of nonparametric techniques in time series can be traced back to the 1940s at least, there still exists healthy and justi?ed skepticism about the capability of nonparametric methods in time series analysis. As - thusiastic explorers of the modern nonparametric toolkit, we feel obliged to assemble together in one place the newly developed relevant techniques. Theaimofthisbookistoadvocatethosemodernnonparametrictechniques that have proven useful for analyzing real time series data, and to provoke further research in both methodology and theory for nonparametric time series analysis. Modern computers and the information age bring us opportunities with challenges. Technological inventions have led to the explosion in data c- lection (e.g., daily grocery sales, stock market trading, microarray data). The Internet makes big data warehouses readily accessible. Although cl- sic parametric models, which postulate global structures for underlying systems, are still very useful, large data sets prompt the search for more re?nedstructures,whichleadstobetterunderstandingandapproximations of the real world. Beyond postulated parametric models, there are in?nite other possibilities. Nonparametric techniques provide useful exploratory tools for this venture, including the suggestion of new parametric models and the validation of existing ones.
  table of contents: Characteristics of Time Series -- ARMA Modeling and Forecasting -- Parametric Nonlinear Time Series Models -- Nonparametric Density Estimation -- Smoothing in Time Series -- Spectral Density Estimation and Its Applications -- Nonparametric Models -- Model Validation -- Nonlinear Prediction.
  note: edited by Jianqing Fan, Qiwei Yao.
  subject:
    topic: Statistics
  subject:
    topic: Economics, Mathematical
  subject:
    topic: Econometrics
  subject:
    topic: Statistics
  subject:
    topic: Statistical Theory and Methods
  subject:
    topic: Quantitative Finance
  subject:
    topic: Econometrics
  classification: QA276-280
  classification: 519.5
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: Springer Series in Statistics
  identifier: 9780387224329
  identifier: http://dx.doi.org/10.1007/b97702
  record info:
    record creation date: 100301
    record change date: 20151204161851.0
    record identifier: 978-0-387-22432-9