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Two methods of robust estimation of a covariance matrix — a practice study for some liver data

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Book cover Advanced Computer Systems

Part of the book series: The Springer International Series in Engineering and Computer Science ((SECS,volume 664))

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Abstract

We consider two methods of computing robust covariance matrices: the hybrid method proposed by Rocke and Woodruff (1996) and the Fast-MCD method proposed by Rousseeuw and van Driessen (1999). We compare the obtained robust covariance matrices both analytically and graphically. The evaluations are done using some medical data. The comparison of the obtained matrices for these data shows, that the two robust methods give systematically slightly differing results, in particular: The MCD method points to more outliers than the Hybrid method proposed by Rocke and Woodruff.

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References

  1. Atkinson, A.C. ‘Stalactite plots and robust estimation for the detection of multivariate outliers’. New Directions in Statistical Data Analysis and Robustness, Eds. S. Morgenthaler, E. Ronchetti, and W.A. Stahel. 1993.

    Google Scholar 

  2. Bartkowiak A., Zdziarek J. 2001. ‘Identifying outliers — a comparative review of 7 methods applied to 7 benchmark data sets’. Manuscript pp. 1–16.

    Google Scholar 

  3. Box G.E.P. 1949. ‘A general Distribution Theory or a Class of Likelihood Criteria’, Biometrika 36, pp 317–346.

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  4. Rocke D.M. 1996. ‘Robustness properties of S-estimators of Multivariate Location and Shape in High Dimension’. Annals of Statistics 24, pp 1327–1345.

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  5. Rocke D., Woodruff D.L. 1996. ‘Identification of outliers in multivariate data’. JASA 91, no 435, pp 1047–1061.

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  6. Rousseeuw P.J., van Driessen K. 1999. ‘A fast algorithm for the minimum covariance determinant estimator’. Technometrics 41, pp 212–223.

    Google Scholar 

  7. Wegman E. J. 1990. ‘Hyperdimensional data analysis using parallel coordinates’. JASA 85, pp. 664–675.

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Jerzy Sołdek Jerzy Pejaś

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© 2002 Springer Science+Business Media New York

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Zdziarek, J. (2002). Two methods of robust estimation of a covariance matrix — a practice study for some liver data. In: Sołdek, J., Pejaś, J. (eds) Advanced Computer Systems. The Springer International Series in Engineering and Computer Science, vol 664. Springer, Boston, MA. https://doi.org/10.1007/978-1-4419-8530-9_6

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  • DOI: https://doi.org/10.1007/978-1-4419-8530-9_6

  • Publisher Name: Springer, Boston, MA

  • Print ISBN: 978-1-4613-4635-7

  • Online ISBN: 978-1-4419-8530-9

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