Journal of the Korean Statistical Society

The Korean Statistical Society (한국통계학회)
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Robustness of Minimum Disparity Estimators in Linear Regression Models

  • Pak, Ro-Jin (Department of Statistics, Taejon University, Taejon 300-716)
  • Published : 1995.12.01
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Abstract

This paper deals with the robustness properties of the minimum disparity estimation in linear regression models. The estimators defined as statistical quantities whcih minimize the blended weight Hellinger distance between a weighted kernel density estimator of the residuals and a smoothed model density of the residuals. It is shown that if the weights of the density estimator are appropriately chosen, the estimates of the regression parameters are robust.

Keywords

References

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  2. Annals of Statistics v.5 Minimum Hellinger Distance Estimates for Parametric Models Beran,R.J.
  3. Annals of Statistics v.1 Robust Regression: Asymptotic, Conjectures, and Monte Calro Huber,P.J.
  4. Robust Statistics Huber,P.J.
  5. Annals of Statistics v.22 Efficiency Versus Robustness: The Case for Minimum Hellinger Distance and Related Methods Lindsay,B.G.
  6. Robust Regression and Outlier Detection Rousseeuw,P.J.;Leroy,A.