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系統識別號 U0026-0812200911135342
論文名稱(中文) 共變數在多重假設檢定上所扮演的角色
論文名稱(英文) The Role of Covariates in Multiple Hypotheses Testing
校院名稱 成功大學
系所名稱(中) 統計學系碩博士班
系所名稱(英) Department of Statistics
學年度 92
學期 2
出版年 93
研究生(中文) 謝其峰
研究生(英文) Chi-Feng Hsieh
電子信箱 stat_normal@hotmail.com
學號 r2691105
學位類別 碩士
語文別 中文
論文頁數 38頁
口試委員 口試委員-陳珍信
口試委員-陳君厚
口試委員-溫敏杰
指導教授-詹世煌
中文關鍵字 偽陽率  逐次 P 值法  Bonferroni 法  多重假設檢定  整體誤差率 
英文關鍵字 false discovery rate  multiple hypotheses testing  sequential p-value approach  familywise error rate  Bonferroni procedure 
學科別分類
中文摘要   對多重假設檢定,傳統上係在控制整體誤差率之下來選取顯著性因子。惟傳統的多重假設檢定,如 Bonferroni 法,雖然程序簡易,但隨著待選因子個數的上升,不易選取到具顯著性的因子,且檢定力低。Benjamini and Hochberg (1995) 提出控制偽陽率 (false positive) 以逐次 P 值法來選取顯著因子,除了改善傳統多重假設檢定的缺點,其執行程序亦相當簡單。惟Benjamini and Hochberg (1995) 在其選取顯著性因子的過程中未考慮潛在的次要因子,此為其中美中不足之處。為了更貼近真實情況,本研究將次要因子加入模型中,用以修正逐次 P 值法。模擬結果證明修正後的逐次 P 值法比其他多重假設檢定方法有較高的檢定力。我們以成大醫院的二組資料說明所建議方法之應用。
英文摘要   Peoele usually need to use the technique of multiple hypotheses testing to search for significant factors under the control of familywise error rate.Traditionally, the procedure of multiple hypotheses testing, like Bonferroni procedure,is very simple and easy to perform, but it tends to select fewer statistically significant facotrs and has smaller power.Benjamini and Hochberg (1995) proposed the sequential p-value approach by controlling the false discovery rate to select statistically significant ones.
This approach of Benjamini and Hochberg (1995) is also simple, and has larger power as compare to traditional method.In the thesis, we consider the sequential p-value approach with covariates adjusted.Through simulation we found that the performances of the adjusted sequential p-value approach is superior to sequential p-value method and Bonferroni approach. We use two real expamles to illustrate the application of the suggested method.
論文目次 1 緒論...............................................5
1.1 研究動機與背景.................................5
1.2 研究架構與目的.................................7
2 多重假設檢定.......................................8
2.1 Bonferroni和其他多重假設檢定法.................9
2.2 偽陽率(False discovery rate, 簡稱FDR)..........10
2.3 逐次P值法......................................11
3 共變數調整之考慮...................................13
3.1 調整原因.......................................13
3.2 共變數對分析的影響.............................14
4 統計模擬...........................................16
4.1 模擬的設定.....................................16
4.1.1 反應變數連續型.............................17
4.1.2 反應變數離散型.............................18
4.2 分析模式.......................................21
4.3 模擬結果.......................................24
4.3.1 反應變數為連續型之結果.....................25
4.3.2 反應變數為離散型之結果.....................25
5 實例分析...........................................33
5.1 實例一:氣喘病.................................33
5.2 實例二:鼻咽癌.................................34
6 結論...............................................37
7 參考文獻...........................................38
參考文獻 1. Benjamini, Y. and Hochberg, Y. (1995) Controlling the False Dis-
covery Rate: A Practical and Powerful Approach to Multiple
Testing. J. Roy. Statist. Soc. Ser. B 57, 289-300.
2. Benjamini, Y. and Yekutieli, D. (2001) The Control of the False
Discovery Rate in the Multiple Testing Under Dependency. Ann.
Statist., 29, 1165-1188.
3. Hommel, G. (1988) A Stagewise Rejective Multiple Test Pro-
cedure Based on a Modi ed Bonferroni Test. Biometrika, 75,
383-386.
4. John D. Storey, (2003) The Postive False Discovery Rate: A
Bayesian Interpretation and the q-value. Ann. Statist., 0, 1-23.
5. Simes, R. J. (1986) An Improved Bonferroni for Multiple Tests
of Signi cance. Biometrika, 73, 751-754.
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