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    题名: Estimating the Number of True Null Hypotheses in Multiple Hypothesis Testing
    作者: 郭訓志
    Hwang, Yi-Ting;Kuo, Hsun-Chih;Wang, Chun-Chao;Lee, Meng Feng
    贡献者: 統計系
    关键词: Adaptive FDR controlling procedure;False discovery rate;Multiple hypothesis testing;Number of true null hypotheses;Sensitivity
    日期: 2013.02
    上传时间: 2013-12-03 18:16:36 (UTC+8)
    摘要: The overall Type I error computed based on the traditional means may be inflated if many hypotheses are compared simultaneously. The family-wise error rate (FWER) and false discovery rate (FDR) are some of commonly used error rates to measure Type I error under the multiple hypothesis setting. Many controlling FWER and FDR procedures have been proposed and have the ability to control the desired FWER/FDR under certain scenarios. Nevertheless, these controlling procedures become too conservative when only some hypotheses are from the null. Benjamini and Hochberg (J. Educ. Behav. Stat. 25:60–83, 2000) proposed an adaptive FDR-controlling procedure that adapts the information of the number of true null hypotheses (m 0) to overcome this problem. Since m 0 is unknown, estimators of m 0 are needed. Benjamini and Hochberg (J. Educ. Behav. Stat. 25:60–83, 2000) suggested a graphical approach to construct an estimator of m 0, which is shown to overestimate m 0 (see Hwang in J. Stat. Comput. Simul. 81:207–220, 2011). Following a similar construction, this paper proposes new estimators of m 0. Monte Carlo simulations are used to evaluate accuracy and precision of new estimators and the feasibility of these new adaptive procedures is evaluated under various simulation settings.
    關聯: Statistics and Computing, Published online: 8 February 2013
    数据类型: article
    DOI 連結: http://dx.doi.org/10.1007/s11222-013-9377-5
    DOI: 10.1007/s11222-013-9377-5
    显示于类别:[統計學系] 期刊論文

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