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Cook's distance in spline smoothing

Author

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  • Kim, Choongrak

Abstract

We present a version of Cook's distance that is applicable to groups of observations in spline smoothing and can be used for outlier detection when masking is present. We express them as functions of the basic diagnostic building blocks: residuals and leverage values. An example based on a real data set is given to illustrate the methods.

Suggested Citation

  • Kim, Choongrak, 1996. "Cook's distance in spline smoothing," Statistics & Probability Letters, Elsevier, vol. 31(2), pages 139-144, December.
    • Handle: RePEc:eee:stapro:v:31:y:1996:i:2:p:139-144
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    Citations

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    Cited by:

    1. Hadi Emami, 2018. "Local influence for Liu estimators in semiparametric linear models," Statistical Papers, Springer, vol. 59(2), pages 529-544, June.
    2. Emami, Hadi, 2015. "Influence diagnostic in ridge semiparametric models," Statistics & Probability Letters, Elsevier, vol. 105(C), pages 106-113.
    3. Michael Martin & Steven Roberts, 2010. "Jackknife-after-bootstrap regression influence diagnostics," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 22(2), pages 257-269.
    4. Kim, Choongrak & Park, Byeong U. & Kim, Woochul, 2002. "Influence diagnostics in semiparametric regression models," Statistics & Probability Letters, Elsevier, vol. 60(1), pages 49-58, November.
    5. Ibacache-Pulgar, Germán & Paula, Gilberto A., 2011. "Local influence for Student-t partially linear models," Computational Statistics & Data Analysis, Elsevier, vol. 55(3), pages 1462-1478, March.
    6. Kim, Choongrak & Lee, Yonjoo & Park, Byeong U., 2001. "Cook's distance in local polynomial regression," Statistics & Probability Letters, Elsevier, vol. 54(1), pages 33-40, August.

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