- 學位論文
以偽發現率為基礎評估逐步向上與逐步向下之程序
Evaluation of Step-up and Step-down Procedures on FDR
摘要
在高維度資料研究中，控制偽發現率（FDR）已快速地被使用 於解決多重性問題。當同時執行大量的假設檢定時，FDR 已經成為 控制型I 誤差率膨脹的重要議題。在多重比較檢定中，傳統上常使 用整體錯誤率（FWER）來控制整體的型I 誤差率。然而，當很多 虛無假設是錯誤的情況下，FWER 會變的太保守以至於降低檢定 力。為了改善FWER 的缺點，Benjamini and Hochberg（1995）提出 較簡易且可提高檢定力的FDR 方法。FDR 有逐步向上與逐步向下 之檢定程序，在本篇論文中，主要的目的在於比較逐步向上與逐步 向下程序的表現，並且指出各個檢定程序的優缺點。模擬的結果指 出，當檢定個數很少和大部分假設都是錯誤時，Benjamini and Liu （1999a、1999b）所提出之方法比其他程序更具有檢定力；而在檢 定個數很多時，Benjamini and Hochberg（1995）程序有較高之檢定 力。
並列摘要
Controlling false discovery rate (FDR) has been increasingly utilized in high dimensional screening studies where the multiplicity is a problem. It becomes an important issue to control the inflating type I error rate when tons of tested hypotheses are simultaneously conducted. Traditionally, familywise error rate (FWER) is used to control the overall type I error in the area of multiple comparison. However, when many null hypotheses are false, FWER tends to be more conservative and has less power. To improve the drawbacks of FWER, a simple approach based on FDR can be used. Two types of FDR procedures for multiple comparison are step-up and step-down procedures. The objective of this article is to compare the performance of current step-up and step-down procedures, and detect the pros and cons of these procedures. The simulated results indicate that Benjamini-Liu (1999a,1999b) procedures are more powerful if the number of tested hypotheses is small and many of the hypotheses are far from true, whereas Benjamini- Hochberg (1995) procedure has large power if the number of tested hypotheses is large.