IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v19y1995i5p519-538.html
   My bibliography  Save this article

Convergence of the feasible solution algorithm for least median of squares regression

Author

Listed:
  • Hawkins, Douglas M.

Abstract

No abstract is available for this item.

Suggested Citation

  • Hawkins, Douglas M., 1995. "Convergence of the feasible solution algorithm for least median of squares regression," Computational Statistics & Data Analysis, Elsevier, vol. 19(5), pages 519-538, May.
    • Handle: RePEc:eee:csdana:v:19:y:1995:i:5:p:519-538
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/0167-9473(94)00017-D
    Download Restriction: Full text for ScienceDirect subscribers only.
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Hawkins, Douglas M., 1994. "The feasible solution algorithm for least trimmed squares regression," Computational Statistics & Data Analysis, Elsevier, vol. 17(2), pages 185-196, February.
    2. 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.
    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. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Leontitsis, Alexandros & Pange, Jenny, 2004. "Statistical significance of the LMS regression," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 64(5), pages 543-547.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    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. & 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.
    4. 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.
    5. Selin Ahipaşaoğlu, 2015. "Fast algorithms for the minimum volume estimator," Journal of Global Optimization, Springer, vol. 62(2), pages 351-370, June.
    6. Nunkesser, Robin & Morell, Oliver, 2008. "Evolutionary algorithms for robust methods," Technical Reports 2008,29, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    7. Sven Jäger & Anita Schöbel, 2020. "The blockwise coordinate descent method for integer programs," Mathematical Methods of Operations Research, Springer;Gesellschaft für Operations Research (GOR);Nederlands Genootschap voor Besliskunde (NGB), vol. 91(2), pages 357-381, April.
    8. Todorov, Valentin & Filzmoser, Peter, 2009. "An Object-Oriented Framework for Robust Multivariate Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 32(i03).
    9. Schyns, M. & Haesbroeck, G. & Critchley, F., 2010. "RelaxMCD: Smooth optimisation for the Minimum Covariance Determinant estimator," Computational Statistics & Data Analysis, Elsevier, vol. 54(4), pages 843-857, April.
    10. Jung, Kang-Mo, 2005. "Multivariate least-trimmed squares regression estimator," Computational Statistics & Data Analysis, Elsevier, vol. 48(2), pages 307-316, February.
    11. Bradu, Dan & Hawkins, Douglas M., 1995. "An Anscombe type robust regression statistic," Computational Statistics & Data Analysis, Elsevier, vol. 20(4), pages 355-386, October.
    12. Pronzato, Luc, 2003. "Removing non-optimal support points in D-optimum design algorithms," Statistics & Probability Letters, Elsevier, vol. 63(3), pages 223-228, July.
    13. Maronna, Ricardo A. & Barrera, Matías Salibian & Yohai, Víctor J., 2000. "Improving bias-robustness of regression estimates through projections," Statistics & Probability Letters, Elsevier, vol. 47(2), pages 149-158, April.
    14. Woodruff, David L. & Reiners, Torsten, 2004. "Experiments with, and on, algorithms for maximum likelihood clustering," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 237-253, September.
    15. Neath, Andrew A. & Cavanaugh, Joseph E., 2000. "A regression model selection criterion based on bootstrap bumping for use with resistant fitting," Computational Statistics & Data Analysis, Elsevier, vol. 35(2), pages 155-169, December.
    16. Nunkesser, Robin & Morell, Oliver, 2010. "An evolutionary algorithm for robust regression," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3242-3248, December.
    17. G. Zioutas & L. Pitsoulis & A. Avramidis, 2009. "Quadratic mixed integer programming and support vectors for deleting outliers in robust regression," Annals of Operations Research, Springer, vol. 166(1), pages 339-353, February.
    18. Hossjer, Ola, 1995. "Exact computation of the least trimmed squares estimate in simple linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 19(3), pages 265-282, March.
    19. Croux, Christophe & Haesbroeck, Gentiane, 1999. "Influence Function and Efficiency of the Minimum Covariance Determinant Scatter Matrix Estimator," Journal of Multivariate Analysis, Elsevier, vol. 71(2), pages 161-190, November.
    20. Mount, David M. & Netanyahu, Nathan S. & Piatko, Christine D. & Wu, Angela Y. & Silverman, Ruth, 2016. "A practical approximation algorithm for the LTS estimator," Computational Statistics & Data Analysis, Elsevier, vol. 99(C), pages 148-170.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:19:y:1995:i:5:p:519-538. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/csda .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.