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Robust estimation of multivariate location and scatter in the presence of cellwise and casewise contamination

Listed author(s):
  • Claudio Agostinelli
  • Andy Leung


  • Victor Yohai
  • Ruben Zamar


Registered author(s):

    Multivariate location and scatter matrix estimation is a cornerstone in multivariate data analysis. We consider this problem when the data may contain independent cellwise and casewise outliers. Flat data sets with a large number of variables and a relatively small number of cases are common place in modern statistical applications. In these cases, global down-weighting of an entire case, as performed by traditional robust procedures, may lead to poor results. We highlight the need for a new generation of robust estimators that can efficiently deal with cellwise outliers and at the same time show good performance under casewise outliers. Copyright Sociedad de Estadística e Investigación Operativa 2015

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    Article provided by Springer & Sociedad de Estadística e Investigación Operativa in its journal TEST.

    Volume (Year): 24 (2015)
    Issue (Month): 3 (September)
    Pages: 441-461

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    Handle: RePEc:spr:testjl:v:24:y:2015:i:3:p:441-461
    DOI: 10.1007/s11749-015-0450-6
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    1. Mike Danilov & Víctor J. Yohai & Ruben H. Zamar, 2012. "Robust Estimation of Multivariate Location and Scatter in the Presence of Missing Data," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 107(499), pages 1178-1186, September.
    2. M. Hubert & P. Rousseeuw & K. Vakili, 2014. "Shape bias of robust covariance estimators: an empirical study," Statistical Papers, Springer, vol. 55(1), pages 15-28, February.
    3. Van Aelst, S. & Vandervieren, E. & Willems, G., 2012. "A Stahel–Donoho estimator based on huberized outlyingness," Computational Statistics & Data Analysis, Elsevier, vol. 56(3), pages 531-542.
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