Minimum Vollume Ellipsoid (MVE) and Minimum Covariance Determinant (MCD) Methods for Estimating Covariance Matrix in Multivariate Data
DOI:
https://doi.org/10.30829/zero.v8i1.20808Keywords:
Covariance Matrix, MCD Method, Multivariate Data, MVE Method, OutlierAbstract
Minimum Volume Ellipsoid (MVE) and Minimum Covariance Determinant (MCD) are robust methods used to handle the outlier problem. Outliers are points that appear to deviate significantly from other data sample points that can have a significant effect on the results of the analysis, so a robust method is needed to solve this problem. MVE and MCD have a high breakdown point or level of resistance to outliers, which is 50%, so that it can overcome the influence of extreme outliers. Based on this research, it is known that by using the same data, the MVE and MCD methods produce more robust estimates that were not affected by outliers. The non robust method just found 10 outliers, while the MVE method found that were 276 data points detected as outliers and for the MCD method, the estimation result is with 257 data points detected as outliers.References
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