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This research work employeda 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 OutlierRobust t-Statistic TORT with the aimof determining the technique that has a higher power of detecting and handling outliersin terms of their P-values, true positives,false positives, False Discovery Rate FDR and their corresponding Receiver OperatingCharacteristic ROC curves. From the result of the analysis, it was revealed thatOS was the best technique followed by COPA, t,ORT, TORT and Z respectively in termsof their P-values. The result of the FalseDiscovery 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 Areaunder the ROC Curve AUC which indicates better sensitivity and specificity andis more significant followed by COPA and ORT with the equal significant AUC while Z and TORT have the least AUC which is notsignificant.

KEYWORDS

Area under the ROC Curve, Reference Line, Sensitivity, Specificity, P-Value, False Discovery Rate FDR, Simulation

Cite this paper

Obikee, A. , Ebuh, G. and Obiora-Ilouno, H. 2014 Comparison of Outlier Techniques Based on Simulated Data. Open Journal of Statistics, 4, 536-561. doi: 10.4236-ojs.2014.47051.





Autor: Adaku C. Obikee, Godday U. Ebuh, Happiness O. Obiora-Ilouno

Fuente: http://www.scirp.org/



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