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題名 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
貢獻者 統計系
關鍵詞 Adaptive FDR controlling procedure ; False discovery rate ; Multiple hypothesis testing ;Number of true null hypotheses ;Sensitivity
日期 2013.02
上傳時間 3-十二月-2013 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
資料類型 article
DOI http://dx.doi.org/10.1007/s11222-013-9377-5
dc.contributor 統計系en_US
dc.creator (作者) 郭訓志zh_TW
dc.creator (作者) Hwang, Yi-Ting ; Kuo, Hsun-Chih ; Wang, Chun-Chao ; Lee, Meng Fengen_US
dc.date (日期) 2013.02en_US
dc.date.accessioned 3-十二月-2013 18:16:36 (UTC+8)-
dc.date.available 3-十二月-2013 18:16:36 (UTC+8)-
dc.date.issued (上傳時間) 3-十二月-2013 18:16:36 (UTC+8)-
dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/62095-
dc.description.abstract (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.en_US
dc.format.extent 762772 bytes-
dc.format.mimetype application/pdf-
dc.language.iso en_US-
dc.relation (關聯) Statistics and Computing, Published online: 8 February 2013en_US
dc.subject (關鍵詞) Adaptive FDR controlling procedure ; False discovery rate ; Multiple hypothesis testing ;Number of true null hypotheses ;Sensitivityen_US
dc.title (題名) Estimating the Number of True Null Hypotheses in Multiple Hypothesis Testingen_US
dc.type (資料類型) articleen
dc.identifier.doi (DOI) 10.1007/s11222-013-9377-5en_US
dc.doi.uri (DOI) http://dx.doi.org/10.1007/s11222-013-9377-5en_US