Association Testing for High-Dimensional Multiple Response Regression
作者机构:School of Mathematics and StatisticsBeijing Institute of TechnologyBeijing 100081China LSCAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing 100190China School of Mathematical ScienceUniversity of Chinese Academy of SciencesBeijing 100190China Institute of Food Science and TechnologyChinese Academy of Agricultural Sciences/Comprehensive Key Laboratory of Agricultural Products Processing and Quality ControlMinistry of AgricultureBeijingChina School of StatisticsCapital University of Economics and BusinessBeijing 100070China
出 版 物:《Journal of Systems Science & Complexity》 (系统科学与复杂性学报(英文版))
年 卷 期:2023年第36卷第4期
页 面:1680-1696页
核心收录:
学科分类:02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 07[理学] 0714[理学-统计学(可授理学、经济学学位)] 070103[理学-概率论与数理统计] 0701[理学-数学]
基 金:This paper was in part supported by China Postdoctoral Science Foundation Funded Project under Grant No.2021M700433 the National Natural Science Foundation of China under Grant Nos.12101047 and 12201432
主 题:Association analysis high-dimensional multiple response multiple response regression non normality
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 ***,both methods have their own *** basis of the former method is independence of multiple responses and the latter one assumes that multiple responses are normally *** this work,the authors propose a test statistic for multiple response association analysis in high-dimensional situations based on F *** is free of normal distribution assumption and the asymptotic normal distribution is obtained under some regular *** computer simulations and four real data applications show its superiority over single-response analysis and MANOVA methods.