Outlier Detection Method in Linear Regression Based on Sum of Arithmetic Progression
Figure 8
Outlier detection from data sets with ten elements. The first element is the reference element, which is not an outlier, where red triangle corresponds to outliers detected by MMS, yellow circle corresponds to outliers detected by EMMS, and green square corresponds to nonoutliers. Value of k for MMS and EMMS is 0.5 and 0.01, respectively. When the reference (first) element is not an outlier, the new method is capable of locating all outliers. 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
