| dc.description.abstract | 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. This study aims to estimate
the covariance matrix that is not affected by outliers in multivariate data using the
MVE and MCD methods. In the MVE method, estimation is done by finding the
smallest ellipsoid that contains most of the data points, which then becomes a
representation of the dataset. Meanwhile, in the MCD method does not use an ellipsoid
that contains h point, but only uses h points to estimate the covariance matrix. 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. For these results, it can be seen that both the MVE and
MCD methods are suitable for estimating the covariance matric in multivariate data. | en_US |
