Comparison of Outlier Techniques Based on Simulated Data

Autor:	Happiness O. Obiora-Ilouno, Adaku C. Obikee, G. U. Ebuh
Rok vydání:	2014
Předmět:	False discovery rate Receiver operating characteristic Simulated data Statistics Outlier False positive paradox Profile analysis p-value Area under the roc curve health care economics and organizations Mathematics
Zdroj:	Open Journal of Statistics. :536-561
ISSN:	2161-7198 2161-718X
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::f8107cb14db33ce3af7ab254d38b4840 https://doi.org/10.4236/ojs.2014.47051 Zobrazit plný text záznamu