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Multivariate outlier detection in Stata

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Author Info

  • 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.

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Bibliographic Info

Article provided by StataCorp LP in its journal Stata Journal.

Volume (Year): 10 (2010)
Issue (Month): 2 (June)
Pages: 259-266

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Handle: RePEc:tsj:stataj:v:10:y:2010:i:2:p:259-266

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Related research

Keywords: mcd; detection; multivariate outliers; robustness; minimum covariance determinant;

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Cited by:
  1. Goetghebuer, Tatiana, 2011. "Productive inefficiency in patriarchal family farms: evidence from Mali," Proceedings of the German Development Economics Conference, Berlin 2011 34, Verein für Socialpolitik, Research Committee Development Economics.

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