IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v34y2000i3p279-298.html
   My bibliography  Save this article

BACON: blocked adaptive computationally efficient outlier nominators

Author

Listed:
  • Billor, Nedret
  • Hadi, Ali S.
  • Velleman, Paul F.

Abstract

No abstract is available for this item.

Suggested Citation

  • Billor, Nedret & Hadi, Ali S. & Velleman, Paul F., 2000. "BACON: blocked adaptive computationally efficient outlier nominators," Computational Statistics & Data Analysis, Elsevier, vol. 34(3), pages 279-298, September.
    • Handle: RePEc:eee:csdana:v:34:y:2000:i:3:p:279-298
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(99)00101-2
    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. Hadi, Ali S., 1992. "A new measure of overall potential influence in linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 14(1), pages 1-27, June.
    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. William Gould & Ali S. Hadi, 1993. "Identifying multivariate outliers," Stata Technical Bulletin, StataCorp LP, vol. 2(11).
    Full references (including those not matched with items on IDEAS)

    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. A.A.M. Nurunnabi & M. Nasser & A.H.M.R. Imon, 2016. "Identification and classification of multiple outliers, high leverage points and influential observations in linear regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(3), pages 509-525, March.
    2. A. H. M. Rahmatullah Imon, 2005. "Identifying multiple influential observations in linear regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 32(9), pages 929-946.
    3. Julia Cage, 2015. "Measuring Policy Performance: Can We Do Better than the World Bank?," Post-Print hal-03392964, HAL.
    4. H. Glendinning, Richard, 2001. "Selecting sub-set autoregressions from outlier contaminated data," Computational Statistics & Data Analysis, Elsevier, vol. 36(2), pages 179-207, April.
    5. Julia Cage, 2009. "Asymmetric information, rent extraction and aid efficiency," PSE Working Papers halshs-00575055, HAL.
    6. Barranco-Chamorro, I. & Jiménez-Gamero, M.D. & Moreno-Rebollo, J.L. & Muñoz-Pichardo, J.M., 2012. "Case-deletion type diagnostics for calibration estimators in survey sampling," Computational Statistics & Data Analysis, Elsevier, vol. 56(7), pages 2219-2236.
    7. 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.
    8. Hoeting, Jennifer & Raftery, Adrian E. & Madigan, David, 1996. "A method for simultaneous variable selection and outlier identification in linear regression," Computational Statistics & Data Analysis, Elsevier, vol. 22(3), pages 251-270, July.
    9. repec:hal:spmain:info:hdl:2441/5su81hd0ma8soqp1nvf7852ffv is not listed on IDEAS
    10. Cagé Julia, 2014. "Improving upon the World Bank’s Country Policy and Institutional Assessment: A New Performance Indicator Based on Aid Effectiveness," Journal of Globalization and Development, De Gruyter, vol. 5(2), pages 213-233, December.
    11. Vilijandas Bagdonavičius & Linas Petkevičius, 2020. "A new multiple outliers identification method in linear regression," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 83(3), pages 275-296, April.
    12. Egger, Peter & Winner, Hannes, 2006. "How Corruption Influences Foreign Direct Investment: A Panel Data Study," Economic Development and Cultural Change, University of Chicago Press, vol. 54(2), pages 459-486, January.
    13. Xavier Cirera & Francesca Foliano & Michael Gasiorek, 2016. "The impact of preferences on developing countries’ exports to the European Union: bilateral gravity modelling at the product level," Empirical Economics, Springer, vol. 50(1), pages 59-102, February.
    14. Kondylis, Athanassios & Hadi, Ali S., 2006. "Derived components regression using the BACON algorithm," Computational Statistics & Data Analysis, Elsevier, vol. 51(2), pages 556-569, November.
    15. M. Suárez-Rancel & Miguel González-Sierra, 2000. "Local and deletion diagnostic," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 9(2), pages 345-352, December.
    16. Irmak Acarlar & Harun Kınacı & Vadoud Najjari, 2014. "A New Measure for Detecting Influential DMUs in DEA," Journal of Optimization, Hindawi, vol. 2014, pages 1-7, October.
    17. Mar Reguant & Meredith Fowlie, 2017. "Measuring and Mitigating Leakage Risk," 2017 Meeting Papers 383, Society for Economic Dynamics.
    18. A.A.M. Nurunnabi & Ali S. Hadi & A.H.M.R. Imon, 2014. "Procedures for the identification of multiple influential observations in linear regression," Journal of Applied Statistics, Taylor & Francis Journals, vol. 41(6), pages 1315-1331, June.
    19. Munoz-Pichardo, J. M. & Enguix-Gonzalez, A. & Munoz-Garcia, J. & Pascual-Acosta, A., 2004. "The Frechet's metric as a measure of influence in multivariate linear models with random errors elliptically distributed," Computational Statistics & Data Analysis, Elsevier, vol. 46(3), pages 469-491, June.
    20. Julia Cage, 2015. "Measuring Policy Performance: Can We Do Better than the World Bank?," SciencePo Working papers hal-03392964, HAL.
    21. 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.

    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:34:y:2000:i:3:p:279-298. 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.