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Diagnostics for penalized least-squares estimators

Author

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  • Eubank, R. L.
  • Gunst, R. F.

Abstract

Diagnostic methods for a class of penalized least-squares estimators are derived from a Bayesian perspective. The class of estimators considered includes generalized ridge estimators, partial splines and thin plate smoothing splines. The proposed diagnostics include scaled residuals, leverage values and various measures of influence.

Suggested Citation

  • Eubank, R. L. & Gunst, R. F., 1986. "Diagnostics for penalized least-squares estimators," Statistics & Probability Letters, Elsevier, vol. 4(5), pages 265-272, August.
    • Handle: RePEc:eee:stapro:v:4:y:1986:i:5:p:265-272
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    Cited by:

    1. Germán Ibacache-Pulgar & Cristian Villegas & Javier Linkolk López-Gonzales & Magaly Moraga, 2023. "Influence measures in nonparametric regression model with symmetric random errors," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 32(1), pages 1-25, March.
    2. 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.
    3. Wei, Wen Hsiang, 2004. "Derivatives diagnostics and robustness for smoothing splines," Computational Statistics & Data Analysis, Elsevier, vol. 46(2), pages 335-356, June.

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