標題: 由混合變異建構基因表現分析之無母數檢定
Nonparametric Test based on Combined Variation for Gene Expression Analysis
作者: 連紫汝
Lian, Tzu-Ju
陳鄰安
Chen, Lin-An
統計學研究所
關鍵字: 基因表現分析;離群平均;離群和;t檢定;Gene expression analysis;Outlier mean;Outlier sum;t-test
公開日期: 2010
摘要: 在致病基因的檢測問題上,因為Tomlins et al.(2005)的發現使得探討離群分配變成一個重要主題。不同於離群平均只能檢測中心位置之改變,我們提出一個統計量它同時可以檢測中心與離心兩種變異。這個統計量還有一個好處。由它所建立的檢定統計量不用估計未知分配的密度函數值。我們利用模擬分析比較了幾種檢定方法的檢力並且做了比較。我們也進一步做了一個簡單的實際資料分析。
Observed by Tomlins et al. (2005), detection of the shift for outlier-
distribution is a new topic useful in gene expression analysis. Alternative to the outlier mean test, we introduce a nonparametric statistic that can simultaneously detect the location shift and variation shift in the outlier distribution. There is an advantage, comparing with the outlier mean, that the test based on this statistic requires no prediction of distributional densities. Comparisons of this test statistic with some other methods in terms of mean square errors for estimation of their population parameters and powers for their abilities in detection of disease genes are simulated and displayed. Finally, a simple real data analysis is also performed and presented.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT079826507
http://hdl.handle.net/11536/47673
Appears in Collections:Thesis


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