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Applied nonparametric methods

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  • HÄRDLE, Wolfgang

    (CORE, Université catholique de Louvain, B-1348 Louvain-la-Neuve, Belgium and CentER, Tilburg)

Abstract

In this note we review different approaches to non parametric regression. Kernel estimators are motivated from local averaging, solving ill-posed problems and weighting of binned data. Kernel estimators are compared to k-NN estimators and splines. The choice of smoothing paramet.er is discussed and finally the method is applied for nonparametric prediction of time series.

Suggested Citation

  • HÄRDLE, Wolfgang, 1992. "Applied nonparametric methods," LIDAM Discussion Papers CORE 1992003, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    • Handle: RePEc:cor:louvco:1992003
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    File URL: https://sites.uclouvain.be/core/publications/coredp/coredp1992.html
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    JEL classification:

    • C39 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Other

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