TITLE:
Comparison of Outlier Techniques Based on Simulated Data
AUTHORS:
Adaku C. Obikee, Godday U. Ebuh, Happiness O. Obiora-Ilouno
KEYWORDS:
Area under the ROC Curve, Reference Line, Sensitivity, Specificity, P-Value, False Discovery Rate (FDR), Simulation
JOURNAL NAME:
Open Journal of Statistics,
Vol.4 No.7,
August
28,
2014
ABSTRACT:
This research work employed
a simulation study to evaluate six outlier techniques: t-Statistic, Modified Z-Statistic,
Cancer Outlier Profile Analysis (COPA), Outlier Sum-Statistic (OS), Outlier Robust t-Statistic (ORT), and the Truncated Outlier
Robust t-Statistic (TORT) with the aim
of determining the technique that has a higher power of detecting and handling outliers
in terms of their P-values, true positives,
false positives, False Discovery Rate (FDR) and their corresponding Receiver Operating
Characteristic (ROC) curves. From the result of the analysis, it was revealed that
OS was the best technique followed by COPA, t,
ORT, TORT and Z respectively in terms
of their P-values. The result of the False
Discovery Rate (FDR) shows that OS is the best technique followed by COPA, t, ORT,
TORT and Z. In terms of their ROC curves, t-Statistic and OS have the largest Area
under the ROC Curve (AUC) which indicates better sensitivity and specificity and
is more significant followed by COPA and ORT with the equal significant AUC while Z and TORT have the least AUC which is not
significant.