| 一个带协变量调整多响应比较的高效方法及其在基因组数据中的应用 |
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| 作者姓名： | 张胜虎 朱家砚 张三国 |
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| 作者单位： | 1. 中国科学院大学数学科学学院, 北京 100049;
2. 中国科学院大数据挖掘与知识管理重点实验室, 北京 100049;
3. 武汉学院信息系, 武汉 430212 |
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| 基金项目： | Supported by Special Fund of University of Chinese Academy of Sciences for Scientific Research Cooperation(Y652022Y00) |
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| 摘 要： | 目前在文献中有很多关于多响应比较的研究方法，但是对带协变量调整的非参数检验的研究较少。一种直观的想法是将数据先投影到协变量的正交空间中，然后再利用秩和检验、调整的秩和检验或最大值检验方法。但是，功效普遍不高。在调整的秩和检验和伪F检验两种方法基础上，构建MIN2检验。大量模拟和实际数据表明，MIN2检验的效果优于现有的非参数检验方法。
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| 关 键 词： | 多响应比较 协变量调整 伪F检验 功效 |
| 收稿时间： | 2018-01-12 |
| 修稿时间： | 2018-03-09 |
A powerful procedure for multiple outcomes comparison with covariate adjustment and its application to genomic data |
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| Authors: | ZHANG Shenghu ZHU Jiayan ZHANG Sanguo |
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| Institution: | 1. School of Mathematical Sciences, University of Chinese Academy of Sciences, Beijing 100049, China;
2. Key Laboratory of Big Data Mining and Knowledge Management of Chinese Academy of Sciences, Beijing 100049, China;
3. School of Information and Communication, Wuhan College, Wuhan 430212, China |
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| Abstract: | Although there are many procedures developed for handling multiple outcomes comparison in the literature, the nonparametric methodology for group comparison with covariate adjustment is still in its infancy. One can use rank-sum test, adjusted rank-sum test, or max-type test by analyzing the processed data orthogonal to the space spanned by covariates. However, the power is not satisfactory. In this work, we combine the adjusted rank-sum test and pseudo F test and then construct a MIN2 test to handle this issue. The performances of MIN2 are thoroughly explored by extensive computer simulations and a real example. |
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| Keywords: | multiple outcomes comparison covariate adjustment pseudo F test" target="_blank">F test')">pseudo F test power |
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