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Modified minimum covariance determinant estimator and its application to outlier detection of chemical process data

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  • Guoqing Wu
  • Chao Chen
  • Xuefeng Yan

Abstract

To overcome the main flaw of minimum covariance determinant (MCD) estimator, i.e. difficulty to determine its main parameter h , a modified-MCD (M-MCD) algorithm is proposed. In M-MCD, the self-adaptive iteration is proposed to minimize the deflection between the standard deviation of robust mahalanobis distance square, which is calculated by MCD with the parameter h based on the sample, and the standard deviation of theoretical mahalanobis distance square by adjusting the parameter h of MCD. Thus, the optimal parameter h of M-MCD is determined when the minimum deflection is obtained. The results of convergence analysis demonstrate that M-MCD has good convergence property. Further, M-MCD and MCD were applied to detect outliers for two typical data and chemical process data, respectively. The results show that M-MCD can get the optimal parameter h by using the self-adaptive iteration and thus its performances of outlier detection are better than MCD.

Suggested Citation

  • Guoqing Wu & Chao Chen & Xuefeng Yan, 2011. "Modified minimum covariance determinant estimator and its application to outlier detection of chemical process data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(5), pages 1007-1020, January.
    • Handle: RePEc:taf:japsta:v:38:y:2011:i:5:p:1007-1020
    DOI: 10.1080/02664761003692456
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    1. Hawkins, Douglas M., 1994. "The feasible solution algorithm for the minimum covariance determinant estimator in multivariate data," Computational Statistics & Data Analysis, Elsevier, vol. 17(2), pages 197-210, February.
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