Association Testing for High-Dimensional Multiple Response Regression

文献类型： 外文期刊

第一作者： Wang, Jinjuan

作者： Wang, Jinjuan;Jiang, Zhenzhen;Jiang, Zhenzhen;Liu, Hongzhi;Meng, Zhen

作者机构：

关键词： Association analysis; high-dimensional multiple response; multiple response regression; non normality

期刊名称：JOURNAL OF SYSTEMS SCIENCE & COMPLEXITY （ 影响因子：2.1； 五年影响因子：1.7 ）

ISSN： 1009-6124

年卷期： 2023 年 36 卷 4 期

页码：

收录情况： SCI

摘要： Multiple response regression model is commonly employed to investigate the relationship between multiple outcomes and a set of potential predictors, where single-response analysis and multivariate analysis of variance (MANOVA) are two frequently used methods for association analysis. However, both methods have their own limitations. The basis of the former method is independence of multiple responses and the latter one assumes that multiple responses are normally distributed. In this work, the authors propose a test statistic for multiple response association analysis in high-dimensional situations based on F statistic. It is free of normal distribution assumption and the asymptotic normal distribution is obtained under some regular conditions. Extensive computer simulations and four real data applications show its superiority over single-response analysis and MANOVA methods.

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