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Robust regression with high coverage

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  • Olive, David J.
  • Hawkins, Douglas M.

Abstract

An important parameter for several high breakdown regression algorithm estimators is the number of cases given weight one, called the coverage of the estimator. Increasing the coverage is believed to result in a more stable estimator, but the price paid for this stability is greatly decreased resistance to outliers. A simple modification of the algorithm can greatly increase the coverage and hence its statistical performance while maintaining high outlier resistance.

Suggested Citation

  • Olive, David J. & Hawkins, Douglas M., 2003. "Robust regression with high coverage," Statistics & Probability Letters, Elsevier, vol. 63(3), pages 259-266, July.
    • Handle: RePEc:eee:stapro:v:63:y:2003:i:3:p:259-266
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    References listed on IDEAS

    as
    1. Hawkins, Douglas M. & Olive, David, 1999. "Applications and algorithms for least trimmed sum of absolute deviations regression," Computational Statistics & Data Analysis, Elsevier, vol. 32(2), pages 119-134, December.
    2. He, Xuming, 1991. "A local breakdown property of robust tests in linear regression," Journal of Multivariate Analysis, Elsevier, vol. 38(2), pages 294-305, August.
    3. Tableman, Mara, 1994. "The influence functions for the least trimmed squares and the least trimmed absolute deviations estimators," Statistics & Probability Letters, Elsevier, vol. 19(4), pages 329-337, March.
    4. Tableman, Mara, 1994. "The asymptotics of the least trimmed absolute deviations (LTAD) estimator," Statistics & Probability Letters, Elsevier, vol. 19(5), pages 387-398, April.
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    Keywords

    Elemental sets LMS LTA LTS Outliers;

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