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Improved feasible solution algorithms for high breakdown estimation

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

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  • Hawkins, Douglas M. & Olive, David J., 1999. "Improved feasible solution algorithms for high breakdown estimation," Computational Statistics & Data Analysis, Elsevier, vol. 30(1), pages 1-11, March.
  • Handle: RePEc:eee:csdana:v:30:y:1999:i:1:p:1-11
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    References listed on IDEAS

    as
    1. Hawkins, Douglas M., 1993. "The feasible set algorithm for least median of squares regression," Computational Statistics & Data Analysis, Elsevier, vol. 16(1), pages 81-101, June.
    2. Croux, Christophe & Haesbroeck, Gentiane, 1997. "An easy way to increase the finite-sample efficiency of the resampled minimum volume ellipsoid estimator," Computational Statistics & Data Analysis, Elsevier, vol. 25(2), pages 125-141, July.
    3. Hawkins, Douglas M., 1994. "The feasible solution algorithm for the minimum covariance determinant estimator in multivariate data," Computational Statistics & Data Analysis, Elsevier, vol. 17(2), pages 197-210, February.
    4. Harrison, David Jr. & Rubinfeld, Daniel L., 1978. "Hedonic housing prices and the demand for clean air," Journal of Environmental Economics and Management, Elsevier, vol. 5(1), pages 81-102, March.
    5. Cook, R. D. & Hawkins, D. M. & Weisberg, S., 1993. "Exact iterative computation of the robust multivariate minimum volume ellipsoid estimator," Statistics & Probability Letters, Elsevier, vol. 16(3), pages 213-218, February.
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    Citations

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    Cited by:

    1. Selin Ahipaşaoğlu, 2015. "Fast algorithms for the minimum volume estimator," Journal of Global Optimization, Springer, vol. 62(2), pages 351-370, June.
    2. Vanessa Berenguer-Rico & Søren Johansen & Bent Nielsen, 2019. "Models where the Least Trimmed Squares and Least Median of Squares estimators are maximum likelihood," CREATES Research Papers 2019-15, Department of Economics and Business Economics, Aarhus University.
    3. Klouda, Karel, 2015. "An exact polynomial time algorithm for computing the least trimmed squares estimate," Computational Statistics & Data Analysis, Elsevier, vol. 84(C), pages 27-40.
    4. L. Pitsoulis & G. Zioutas, 2010. "A fast algorithm for robust regression with penalised trimmed squares," Computational Statistics, Springer, vol. 25(4), pages 663-689, December.
    5. Nunkesser, Robin & Morell, Oliver, 2008. "Evolutionary algorithms for robust methods," Technical Reports 2008,29, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    6. J. L. Alfaro & J. Fco. Ortega, 2009. "A comparison of robust alternatives to Hotelling's T2 control chart," Journal of Applied Statistics, Taylor & Francis Journals, vol. 36(12), pages 1385-1396.
    7. Hardin, Johanna & Rocke, David M., 2004. "Outlier detection in the multiple cluster setting using the minimum covariance determinant estimator," Computational Statistics & Data Analysis, Elsevier, vol. 44(4), pages 625-638, January.
    8. Nunkesser, Robin & Morell, Oliver, 2010. "An evolutionary algorithm for robust regression," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3242-3248, December.
    9. Garcia-Escudero, L.A. & Gordaliza, A., 2007. "The importance of the scales in heterogeneous robust clustering," Computational Statistics & Data Analysis, Elsevier, vol. 51(9), pages 4403-4412, May.
    10. Ranganai, Edmore, 2016. "Quality of fit measurement in regression quantiles: An elemental set method approach," Statistics & Probability Letters, Elsevier, vol. 111(C), pages 18-25.
    11. Agullo, Jose, 2001. "New algorithms for computing the least trimmed squares regression estimator," Computational Statistics & Data Analysis, Elsevier, vol. 36(4), pages 425-439, June.
    12. Olive, David J., 2004. "A resistant estimator of multivariate location and dispersion," Computational Statistics & Data Analysis, Elsevier, vol. 46(1), pages 93-102, May.

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