IDEAS home Printed from https://ideas.repec.org/a/eee/csdana/v52y2007i1p309-324.html
   My bibliography  Save this article

Kernel ellipsoidal trimming

Author

Listed:
  • Dolia, A.N.
  • Harris, C.J.
  • Shawe-Taylor, J.S.
  • Titterington, D.M.

Abstract

No abstract is available for this item.

Suggested Citation

  • Dolia, A.N. & Harris, C.J. & Shawe-Taylor, J.S. & Titterington, D.M., 2007. "Kernel ellipsoidal trimming," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 309-324, September.
    • Handle: RePEc:eee:csdana:v:52:y:2007:i:1:p:309-324
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0167-9473(07)00115-6
    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. D. M. Titterington, 1978. "Estimation of Correlation Coefficients by Ellipsoidal Trimming," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 27(3), pages 227-234, November.
    2. Woodward, Wayne A. & Sain, Stephan R., 2003. "Testing for outliers from a mixture distribution when some data are missing," Computational Statistics & Data Analysis, Elsevier, vol. 44(1-2), pages 193-210, October.
    3. 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.
    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. Chiwoo Park & Jianhua Z. Huang & Yu Ding, 2010. "A Computable Plug-In Estimator of Minimum Volume Sets for Novelty Detection," Operations Research, INFORMS, vol. 58(5), pages 1469-1480, October.

    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. Michael S. Delgado & Daniel J. Henderson & Christopher F. Parmeter, 2014. "Does Education Matter for Economic Growth?," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 76(3), pages 334-359, June.
    2. Karim Abou-Moustafa & Frank P. Ferrie, 2018. "Local generalized quadratic distance metrics: application to the k-nearest neighbors classifier," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 12(2), pages 341-363, June.
    3. Chrys Caroni & Nedret Billor, 2007. "Robust Detection of Multiple Outliers in Grouped Multivariate Data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 34(10), pages 1241-1250.
    4. Yu, Yaming, 2010. "Strict monotonicity and convergence rate of Titterington's algorithm for computing D-optimal designs," Computational Statistics & Data Analysis, Elsevier, vol. 54(6), pages 1419-1425, June.
    5. Van Aelst, Stefan & (Steven) Wang, Xiaogang & Zamar, Ruben H. & Zhu, Rong, 2006. "Linear grouping using orthogonal regression," Computational Statistics & Data Analysis, Elsevier, vol. 50(5), pages 1287-1312, March.
    6. Martin-Martin, R. & Torsney, B. & Lopez-Fidalgo, J., 2007. "Construction of marginally and conditionally restricted designs using multiplicative algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 51(12), pages 5547-5561, August.
    7. Fritz, Heinrich & García-Escudero, Luis A. & Mayo-Iscar, Agustín, 2012. "tclust: An R Package for a Trimming Approach to Cluster Analysis," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 47(i12).
    8. Pronzato, Luc, 2003. "Removing non-optimal support points in D-optimum design algorithms," Statistics & Probability Letters, Elsevier, vol. 63(3), pages 223-228, July.
    9. Alper Sinan & B. Barıs Alkan, 2015. "A useful approach to identify the multicollinearity in the presence of outliers," Journal of Applied Statistics, Taylor & Francis Journals, vol. 42(5), pages 986-993, May.
    10. Peter Goos & Bradley Jones & Utami Syafitri, 2016. "I-Optimal Design of Mixture Experiments," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 111(514), pages 899-911, April.
    11. Nolan, D., 1999. "On min-max majority and deepest points," Statistics & Probability Letters, Elsevier, vol. 43(4), pages 325-333, July.
    12. Neykov, N. & Filzmoser, P. & Dimova, R. & Neytchev, P., 2007. "Robust fitting of mixtures using the trimmed likelihood estimator," Computational Statistics & Data Analysis, Elsevier, vol. 52(1), pages 299-308, September.
    13. Alexander A. Aduenko & Anastasia P. Motrenko & Vadim V. Strijov, 2018. "Object selection in credit scoring using covariance matrix of parameters estimations," Annals of Operations Research, Springer, vol. 260(1), pages 3-21, January.
    14. Dette, Holger & Pepelyshev, Andrey & Zhigljavsky, Anatoly, 2008. "Improving updating rules in multiplicative algorithms for computing D-optimal designs," Computational Statistics & Data Analysis, Elsevier, vol. 53(2), pages 312-320, December.
    15. Petr Hajek & Mohammad Zoynul Abedin & Uthayasankar Sivarajah, 2023. "Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework," Information Systems Frontiers, Springer, vol. 25(5), pages 1985-2003, October.
    16. Rosa, Samuel & Harman, Radoslav, 2022. "Computing minimum-volume enclosing ellipsoids for large datasets," Computational Statistics & Data Analysis, Elsevier, vol. 171(C).
    17. Bernard, Carole & Vanduffel, Steven, 2015. "A new approach to assessing model risk in high dimensions," Journal of Banking & Finance, Elsevier, vol. 58(C), pages 166-178.
    18. Beirlant, J. & Mason, D. M. & Vynckier, C., 1999. "Goodness-of-fit analysis for multivariate normality based on generalized quantiles," Computational Statistics & Data Analysis, Elsevier, vol. 30(2), pages 119-142, April.
    19. Zhangpeng Gao & Shahidur Rahman, 2006. "A New Direction of Fund Rating Based on the Finite Normal Mixture Model," Economic Growth Centre Working Paper Series 0603, Nanyang Technological University, School of Social Sciences, Economic Growth Centre.
    20. Alessio Farcomeni, 2009. "Robust Double Clustering: A Method Based on Alternating Concentration Steps," Journal of Classification, Springer;The Classification Society, vol. 26(1), pages 77-101, April.

    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:52:y:2007:i:1:p:309-324. 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.