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Multivariate outlier detection in Stata

Author

Listed:
  • Vincenzo Verardi

    (University of Namur)

  • Catherine Dehon

    (Universite libre de Bruxelles)

Abstract

Before implementing any multivariate statistical analysis based on em- pirical covariance matrices, it is important to check whether outliers are present because their existence could induce significant biases. In this article, we present the minimum covariance determinant estimator, which is commonly used in ro- bust statistics to estimate location parameters and multivariate scales. These estimators can be used to robustify Mahalanobis distances and to identify outliers. Verardi and Croux (1999, Stata Journal 9: 439–453; 2010, Stata Journal 10: 313) programmed this estimator in Stata and made it available with the mcd command. The implemented algorithm is relatively fast and, as we show in the simulation example section, outperforms the methods already available in Stata, such as the Hadi method. Copyright 2010 by StataCorp LP.

Suggested Citation

  • Vincenzo Verardi & Catherine Dehon, 2010. "Multivariate outlier detection in Stata," Stata Journal, StataCorp LP, vol. 10(2), pages 259-266, June.
    • Handle: RePEc:tsj:stataj:v:10:y:2010:i:2:p:259-266
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    References listed on IDEAS

    as
    1. Vincenzo Verardi & Christophe Croux, 2009. "Robust regression in Stata," Stata Journal, StataCorp LP, vol. 9(3), pages 439-453, September.
    2. Hubert, Mia & Van Driessen, Katrien, 2004. "Fast and robust discriminant analysis," Computational Statistics & Data Analysis, Elsevier, vol. 45(2), pages 301-320, March.
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