IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v36y2001i4p425-439.html
   My bibliography  Save this article

New algorithms for computing the least trimmed squares regression estimator

Author

Listed:
  • Agullo, Jose

Abstract

No abstract is available for this item.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:csdana:v:36:y:2001:i:4:p:425-439
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(00)00056-6
    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. Douglas M. Hawkins & Jeffrey S. Simonoff, 1993. "High Breakdown Regression and Multivariate Estimation," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 42(2), pages 423-432, June.
    3. W. Morven Gentleman, 1974. "Basic Procedures for Large, Sparse or Weighted Linear Least Squares Problems," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 23(3), pages 448-454, November.
    4. 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.
    5. 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.
    6. Hossjer, O. & Croux, C. & Rousseeuw, P. J., 1994. "Asymptotics of Generalized S-Estimators," Journal of Multivariate Analysis, Elsevier, vol. 51(1), pages 148-177, October.
    7. 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.
    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. Čížek, Pavel, 2008. "General Trimmed Estimation: Robust Approach To Nonlinear And Limited Dependent Variable Models," Econometric Theory, Cambridge University Press, vol. 24(6), pages 1500-1529, December.
    2. Cizek, P., 2007. "General Trimmed Estimation : Robust Approach to Nonlinear and Limited Dependent Variable Models (Replaces DP 2007-1)," Other publications TiSEM eeccf622-dd18-41d4-a2f9-b, Tilburg University, School of Economics and Management.
    3. Cizek, P., 2004. "Asymptotics of Least Trimmed Squares Regression," Discussion Paper 2004-72, Tilburg University, Center for Economic Research.
    4. Nguyen, T.D. & Welsch, R., 2010. "Outlier detection and least trimmed squares approximation using semi-definite programming," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3212-3226, December.
    5. Nunkesser, Robin & Morell, Oliver, 2010. "An evolutionary algorithm for robust regression," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3242-3248, December.
    6. Flores, Salvador, 2010. "On the efficient computation of robust regression estimators," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3044-3056, December.

    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, 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. Nunkesser, Robin & Morell, Oliver, 2008. "Evolutionary algorithms for robust methods," Technical Reports 2008,29, Technische Universität Dortmund, Sonderforschungsbereich 475: Komplexitätsreduktion in multivariaten Datenstrukturen.
    3. Nunkesser, Robin & Morell, Oliver, 2010. "An evolutionary algorithm for robust regression," Computational Statistics & Data Analysis, Elsevier, vol. 54(12), pages 3242-3248, December.
    4. Yijun Zuo, 2024. "Large Sample Behavior of the Least Trimmed Squares Estimator," Mathematics, MDPI, vol. 12(22), pages 1-21, November.
    5. 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.
    6. Jianhong Shi & Qian Yang & Xiongya Li & Weixing Song, 2017. "Effects of measurement error on a class of single-index varying coefficient regression models," Computational Statistics, Springer, vol. 32(3), pages 977-1001, September.
    7. Villalonga, Belen, 2004. "Intangible resources, Tobin's q, and sustainability of performance differences," Journal of Economic Behavior & Organization, Elsevier, vol. 54(2), pages 205-230, June.
    8. Brockmeier, M., 1991. "Entwicklung und Aufhebung von Reinheitsgeboten im Nahrungsmittelbereich – Analyse und Bewertung," Proceedings “Schriften der Gesellschaft für Wirtschafts- und Sozialwissenschaften des Landbaues e.V.”, German Association of Agricultural Economists (GEWISOLA), vol. 27.
    9. Miller, Steve & Startz, Richard, 2019. "Feasible generalized least squares using support vector regression," Economics Letters, Elsevier, vol. 175(C), pages 28-31.
    10. Umberto Amato & Anestis Antoniadis & Italia De Feis & Irene Gijbels, 2021. "Penalised robust estimators for sparse and high-dimensional linear models," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 30(1), pages 1-48, March.
    11. Prendergast, Luke A. & Li Wai Suen, Connie, 2011. "A new and practical influence measure for subsets of covariance matrix sample principal components with applications to high dimensional datasets," Computational Statistics & Data Analysis, Elsevier, vol. 55(1), pages 752-764, January.
    12. Tizheng Li & Xiaojuan Kang, 2022. "Variable selection of higher-order partially linear spatial autoregressive model with a diverging number of parameters," Statistical Papers, Springer, vol. 63(1), pages 243-285, February.
    13. Deac Dan Stelian & Schebesch Klaus Bruno, 2018. "Market Forecasts and Client Behavioral Data: Towards Finding Adequate Model Complexity," Studia Universitatis „Vasile Goldis” Arad – Economics Series, Sciendo, vol. 28(3), pages 50-75, September.
    14. Juan Ignacio Zoloa, 2020. "Noise pollution and housing markets: A spatial hedonic analysis for La Plata City," Ensayos de Política Económica, Departamento de Investigación Francisco Valsecchi, Facultad de Ciencias Económicas, Pontificia Universidad Católica Argentina., vol. 3(2), pages 129-152, Octubre.
    15. 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.
    16. Cheng, Tsung-Chi, 2012. "On simultaneously identifying outliers and heteroscedasticity without specific form," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2258-2272.
    17. Bodhisattva Sen & Mary Meyer, 2017. "Testing against a linear regression model using ideas from shape-restricted estimation," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 79(2), pages 423-448, March.
    18. Benítez-Peña, Sandra & Blanquero, Rafael & Carrizosa, Emilio & Ramírez-Cobo, Pepa, 2024. "Cost-sensitive probabilistic predictions for support vector machines," European Journal of Operational Research, Elsevier, vol. 314(1), pages 268-279.
    19. repec:wyi:journl:002176 is not listed on IDEAS
    20. Steve Gibbons & Stephan Heblich & Esther Lho & Christopher Timmins, 2016. "Fear of Fracking? The Impact of the Shale Gas Exploration on House Prices in Britain," SERC Discussion Papers 0207, Centre for Economic Performance, LSE.
    21. Sanying Feng & Liugen Xue, 2014. "Bias-corrected statistical inference for partially linear varying coefficient errors-in-variables models with restricted condition," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 66(1), pages 121-140, February.

    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:36:y:2001:i:4:p:425-439. 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.