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The complementary use of regression diagnostics and robust estimators

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  • Diane I. Gibbons
  • Gary C. McDonald
  • Richard F. Gunst

Abstract

Regression modeling for prediction or forecasting purposes is critically dependent on the quality of the data which are used to estimate the model parameters. Extreme response or predictor‐variable values can substantially influence least‐squares estimates and disproportionately affect predictions. Robust alternatives to least‐squares are less sensitive to extreme observations and can provide more precise predictions. In this article diagnostic displays are used to identify extreme observations and to assess the sensitivity of least‐squares parameter estimates and predictions to the inclusion of these observations in a data set. The displays are shown to aid in the interpretation of weights which robust estimators assign to influential observations.

Suggested Citation

  • Diane I. Gibbons & Gary C. McDonald & Richard F. Gunst, 1987. "The complementary use of regression diagnostics and robust estimators," Naval Research Logistics (NRL), John Wiley & Sons, vol. 34(1), pages 109-131, February.
    • Handle: RePEc:wly:navres:v:34:y:1987:i:1:p:109-131
    DOI: 10.1002/1520-6750(198702)34:13.0.CO;2-1
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    Cited by:

    1. A. Pedro Duarte Silva & Antonie Stam & John Neter, 1997. "Two-Group Classification in Business: an Evaluation of Paramatric and Non-Parametric Approaches," Working Papers de Economia (Economics Working Papers) 03, Católica Porto Business School, Universidade Católica Portuguesa.
    2. Diane I. Gibbons & Gary C. McDonald, 1988. "A graphical aid for sensitivity analysis of estimated regression coefficients," Naval Research Logistics (NRL), John Wiley & Sons, vol. 35(2), pages 269-283, April.

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