| 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 Feng | en_US |
| dc.date (日期) | 2013.02 | en_US |
| dc.date.accessioned | 3-Dec-2013 18:16:36 (UTC+8) | - |
| dc.date.available | 3-Dec-2013 18:16:36 (UTC+8) | - |
| dc.date.issued (上傳時間) | 3-Dec-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 2013 | en_US |
| dc.subject (關鍵詞) | Adaptive FDR controlling procedure ; False discovery rate ; Multiple hypothesis testing ;Number of true null hypotheses ;Sensitivity | en_US |
| dc.title (題名) | Estimating the Number of True Null Hypotheses in Multiple Hypothesis Testing | en_US |
| dc.type (資料類型) | article | en |
| dc.identifier.doi (DOI) | 10.1007/s11222-013-9377-5 | en_US |
| dc.doi.uri (DOI) | http://dx.doi.org/10.1007/s11222-013-9377-5 | en_US |
