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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
    出,當檢定個數很少和大部分假設都是錯誤時,Benjamini and Liu
    定個數很多時,Benjamini and Hochberg(1995)程序有較高之檢定
    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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