Detection of Outliers in Multivariate Data using Minimum Vector Variance Method

Abstract

Outliers are observations that do not follow the distribution of data patterns and can cause deviations from data analysis, so a method for identifying outliers is needed One method in scanning detection is Minimum Vector Variance which is a robust estimator that uses the minimum Vector Variance (VV) criteria. In this study, the MVV method was used to detect outliers in criminality data in Indonesia in 2013 and data that had been entered out by 5% and 10%. The results showed that the MVV method was more effective than the Mahalanobis distance when detecting outliers in data that had been entered out by 5% and 10%.