Comparison of Outlier Techniques Based on Simulated Data

• 作者: Adaku C. Obikee Godday U. Ebuh Happiness O. Obiora-Ilouno
• 来源期刊: 统计学期刊(英文)
• 学科: 医学
• 关键词: Area under the ROC CURVE Reference Line Sensitivity SPECIFICITY P-VALUE False Discovery Rate (FDR) Simulation
• 年，卷（期）: 2014,（7）
• 页码范围: 536-561
• 页数: 26页
• 分类号: R73

摘要

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.