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Comparing Local Fitting to Other Automatic Smoothers

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Statistical Modelling

Part of the book series: Lecture Notes in Statistics ((LNS,volume 104))

Abstract

Two automatic local fitting procedures are compared by simulation. They appear to exhibit similar small sample behaviour as other more popular smoothing methods.

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References

  • Aptech Systems, Inc., Maple Valley (1993). GAUSS Volume 2, Command Reference.

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  • Cleveland, W.S. (1979). Robust locally weighted regression and smoothing scatterplots. Journal of the American Statistical Association, 74(368): 829–836.

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  • Fedorov, V.V., P. Hackl, Müller, W.G. (1994). Optimized moving local re-gression: Computational methods for the moving average case. In Dutter, R., Grossmann, W., editors, COMPSTAT: Proceedings in Computational Statistics, pages 476–481. Physica.

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  • Maderbacher, M., Müller, W.G. (1995). Comparing local fitting to other automatic smoothers (with full tables). Technical Report #45, Institut für Statistik, Wirtschaftsuniversität Wien.

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  • Maderbacher, M. (1994). Vergleich automatischer Glättungsverfahren und Implementierung der gleitenden lokalen Regression in GAUSS. Master’s thesis, Wirtschaftsuniversität Wien.

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Authors
  1. Michael Maderbacher
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  2. Werner G. Müller
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Editor information

Editors and Affiliations

  1. Institut für Statistik, Universität Innsbruck, Christoph-Probst-Platz, Innsbruck, A-6020, Austria

    Gilg U. H. Seeber  & Gabriele Steckel-Berger  & 

  2. Centre for Applied Statistics, Lancaster University Fylde College, Lancaster, LA1 4YF, England

    Brian J. Francis

  3. Institut für Statistik, Wirtschaftsuniversität, Augasse 2-6, Wien, A-1090, Austria

    Reinhold Hatzinger

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© 1995 Springer Science+Business Media New York

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Maderbacher, M., Müller, W.G. (1995). Comparing Local Fitting to Other Automatic Smoothers. In: Seeber, G.U.H., Francis, B.J., Hatzinger, R., Steckel-Berger, G. (eds) Statistical Modelling. Lecture Notes in Statistics, vol 104. Springer, New York, NY. https://doi.org/10.1007/978-1-4612-0789-4_20

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  • DOI: https://doi.org/10.1007/978-1-4612-0789-4_20

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-0-387-94565-1

  • Online ISBN: 978-1-4612-0789-4

  • eBook Packages: Springer Book Archive

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