Outlier Detection Method in Linear Regression Based on Sum of Arithmetic Progression
Figure 9
Outlier detection from data sets with ten elements. The first element is the reference element, which is an outlier, where red triangle corresponds to outliers detected by MMS, yellow circle corresponds to outliers detected by EMMS, green square corresponds to nonoutliers, and black arrow corresponds to wrong detections. Value of k for MMS and EMMS is 0.5 and 0.01, respectively. When the reference (first) element is an outlier and outliers are non-Gaussian, the new method identifies only the significant outliers ((a), (b), (c)). When the outliers are Gaussian, MMS automatically becomes inactive (now no significant outliers) ((d), (e), (f)).
(a) Data type: increment, outlier type: non-Gaussian
(b) Data type: decrement, outlier type: non-Gaussian
(c) Data type: constant, outlier type: non-Gaussian
