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Presentation of smoothers: the family approach

Author

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  • J. S. Marron

    (University of North Carolina)

  • S. S. Chung

    (Chonbuk University)

Abstract

Summary The product of most statistical smoothing methods is a single curve estimate. A drawback of such methods is that what is learned varies with choice of the smoothing parameter. This paper proposes simultaneous display of all important features, through presentation of a family of smooths. Some suggestions are given as to how this should be done.

Suggested Citation

  • J. S. Marron & S. S. Chung, 2001. "Presentation of smoothers: the family approach," Computational Statistics, Springer, vol. 16(1), pages 195-207, March.
    • Handle: RePEc:spr:compst:v:16:y:2001:i:1:d:10.1007_s001800100059
    DOI: 10.1007/s001800100059
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    References listed on IDEAS

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    1. PARK, Byeong U. & TURLACH, Berwin A., 1992. "Practical performance of several data driven bandwidth selectors," LIDAM Reprints CORE 1001, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    2. Park, B. & Turlach, B., 1992. "Practical Performance of Several Data Driven Bandwidih Selectors," Papers 9203, Catholique de Louvain - Institut de statistique.
    3. PARK, Byeong & TURLACH, Berwin, 1992. "Practical performance of several data driven bandwidth selectors," LIDAM Discussion Papers CORE 1992005, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    4. Park, B.U. & Turlach, B.A., 1992. "Rejoinder to ``Practical performance of several data driven bandwidth selectors"," LIDAM Reprints CORE 1022, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Kooperberg, Charles & Stone, Charles J., 1991. "A study of logspline density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 12(3), pages 327-347, November.
    6. J. S. Marron & Frederic Udina, 1995. "Interactive local bandwidth choice," Economics Working Papers 109, Department of Economics and Business, Universitat Pompeu Fabra.
    7. Peter Diggle, 1985. "A Kernel Method for Smoothing Point Process Data," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 34(2), pages 138-147, June.
    8. Cao, Ricardo & Cuevas, Antonio & Gonzalez Manteiga, Wensceslao, 1994. "A comparative study of several smoothing methods in density estimation," Computational Statistics & Data Analysis, Elsevier, vol. 17(2), pages 153-176, February.
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    Cited by:

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    2. Lasse Holmström & Leena Pasanen, 2017. "Statistical Scale Space Methods," International Statistical Review, International Statistical Institute, vol. 85(1), pages 1-30, April.

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