Outlier Detection Method in Linear Regression Based on Sum of Arithmetic Progression
Figure 10
Two artificial data samples with 1000 elements each, including 50, 100, 100, and 50 (total 300) missing value regions. The first element is the reference element, which is not an outlier, where (a) corresponds to a data set with outliers in non-Gaussian, (b) corresponds to a data set with outliers in nearly Gaussian, red triangle corresponds to outliers detected by MMS, yellow circle corresponds to outliers detected by EMMS, and green square corresponds to nonoutliers. The value of k for MMS and EMMS is 0.5 and 0.01, respectively. The new method was able to identify all the elements related to the line with 0% error.
