Advances in Operations Research / 2011 / Article / Tab 1 / Research Article
Outlier-Resistant
𝐿
𝟏
Orthogonal Regression via the Reformulation-Linearization Technique Table 1 Mean (standard deviation) of
𝐷
for
𝐿
1
OR,
𝐿
2
OR, ppOR-mad, ppOR-qn,
𝜏
-OGK, and PCA-
𝐿
1
with contamination magnitudes
𝑚
=
1
, 1010, and 5050 and contamination levels
𝐶
=
0
, 10, and 25.
Method
𝑚
=
1
𝑚
=
1
0
𝑚
=
5
0
𝐶
=
0
𝐿
1
OR 0.00997 (0.00540)
𝐿
2
OR 0.01818 (0.01459) ppOR-mad 0.13624 (0.09616) ppOR-qn 0.08398 (0.07724)
𝜏
-OGK 0.01870 (0.01486) PCA-
𝐿
1
0.02081 (0.01388)
𝐶
=
1
0
𝐿
1
OR 0.00934 (0.00583) 0.08496 (0.01798) 0.31339 (0.05527)
𝐿
2
OR 0.03070 (0.01578) 0.32365 (0.10149) 3.54666 (0.99552) ppOR-mad 0.13714 (0.12535) 0.11584 (0.10239) 0.08906 (0.07094) ppOR-qn 0.07475 (0.06369) 0.14938 (0.08210) 0.05840 (0.04831)
𝜏
-OGK 0.03018 (0.01696) 0.18032 (0.03857) 0.20396 (0.03736) PCA-
𝐿
1
0.02608 (0.01667) 0.17335 (0.04240) 0.76836 (0.16126)
𝐶
=
2
5
𝐿
1
OR 0.01190 (0.00573) 0.16172 (0.02743) 0.58962 (0.06106)
𝐿
2
OR 0.04505 (0.01420) 0.62263 (0.12630) 6.26558 (1.35709) ppOR-mad 0.12443 (0.10311) 0.25518 (0.24805) 0.31136 (0.28315) ppOR-qn 0.08947 (0.08796) 0.59031 (0.18792) 0.24970 (0.12092)
𝜏
-OGK 0.03865 (0.01879) 0.45040 (0.09105) 0.54522 (0.08887) PCA-
𝐿
1
0.03940 (0.01382) 0.35664 (0.06198) 1.87768 (0.31515)
