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Multivariate outlier detection based on a robust Mahalanobis distance with shrinkage estimators

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  • Cabana Garceran del Vall, Elisa
  • Laniado Rodas, Henry
  • Lillo Rodríguez, Rosa Elvira

Abstract

A collection of methods for multivariate outlier detection based on a robust Mahalanobis distance is proposed. The procedure consists on different combinations of robust estimates for location and covariance matrix based on shrinkage. The performance of our proposal is illustrated, through the comparison to other techniques from the literature, in a simulation study. The resulting high correct classification rates and low false classification rates in the vast majority of cases, and also the good computational times shows the goodness of our proposal. The performance is also illustrated with a real dataset example and some conclusions are established.

Suggested Citation

  • Cabana Garceran del Vall, Elisa & Laniado Rodas, Henry & Lillo Rodríguez, Rosa Elvira, 2017. "Multivariate outlier detection based on a robust Mahalanobis distance with shrinkage estimators," DES - Working Papers. Statistics and Econometrics. WS 24613, Universidad Carlos III de Madrid. Departamento de Estadística.
    • Handle: RePEc:cte:wsrepe:24613
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    References listed on IDEAS

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    1. DeMiguel, Victor & Martin-Utrera, Alberto & Nogales, Francisco J., 2013. "Size matters: Optimal calibration of shrinkage estimators for portfolio selection," Journal of Banking & Finance, Elsevier, vol. 37(8), pages 3018-3034.
    2. Michael Falk, 1997. "On Mad and Comedians," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 49(4), pages 615-644, December.
    3. Davy Paindaveine & Germain Van bever, 2013. "From Depth to Local Depth: A Focus on Centrality," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 108(503), pages 1105-1119, September.
    4. Arup Bose & Probal Chaudhuri, 1993. "On the dispersion of multivariate median," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 45(3), pages 541-550, September.
    5. Arup Bose, 1995. "Estimating the asymptotic dispersion of theL 1 median," Annals of the Institute of Statistical Mathematics, Springer;The Institute of Statistical Mathematics, vol. 47(2), pages 267-271, June.
    6. Dodge, Yadolah, 1987. "An introduction to L1-norm based statistical data analysis," Computational Statistics & Data Analysis, Elsevier, vol. 5(4), pages 239-253, September.
    7. Cator, Eric A. & Lopuhaä, Hendrik P., 2010. "Asymptotic expansion of the minimum covariance determinant estimators," Journal of Multivariate Analysis, Elsevier, vol. 101(10), pages 2372-2388, November.
    8. Tarr, G. & Müller, S. & Weber, N.C., 2016. "Robust estimation of precision matrices under cellwise contamination," Computational Statistics & Data Analysis, Elsevier, vol. 93(C), pages 404-420.
    9. Chen, Song Xi & Qin, Yingli, 2010. "A Two Sample Test for High Dimensional Data with Applications to Gene-set Testing," MPRA Paper 59642, University Library of Munich, Germany.
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    outlier detection;

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