@article{Verardi:152312,
      recid = {152312},
      author = {Verardi, Vincenzo and Dehon, Catherine},
      title = {Multivariate outlier detection in Stata},
      journal = {Stata Journal},
      address = {2010},
      number = {199-2016-2685},
      pages = {8},
      year = {2010},
      abstract = {Before implementing any multivariate statistical analysis  based on empirical 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 robust 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.},
      url = {http://ageconsearch.umn.edu/record/152312},
      doi = {https://doi.org/10.22004/ag.econ.152312},
}