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    Please use this identifier to cite or link to this item: http://tkuir.lib.tku.edu.tw:8080/dspace/handle/987654321/33878


    Title: Evaluation of step-up and step-down procedures on fdr
    Other Titles: 以偽發現率為基礎評估逐步向上與逐步向下之程序
    Authors: 高子惠;Kao, Tzu-hui
    Contributors: 淡江大學統計學系碩士班
    陳怡如;Chen, Yi-ju
    Keywords: 偽發現率;多重比較;型I 誤差率;檢定力;FDR;multiple comparison;type I error rate;power
    Date: 2009
    Issue Date: 2010-01-11 04:37:33 (UTC+8)
    Abstract: 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。
    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.
    Appears in Collections:[統計學系暨研究所] 學位論文

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