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	Integrative interaction analysis using threshold gradient directed regularization
	Li, Yang1,2; Li, Rong2; Qin, Yichen3; Wu, Mengyun4,5; Ma, Shuangge2,5
	2019-03-01
关键词	high-dimensional data integrative analysis interaction analysis TGDR
卷号	35
期号	2
DOI	10.1002/asmb.2342
页码	354-375
英文摘要	For many complex business and industry problems, high-dimensional data collection and modeling have been conducted. It has been shown that interactions may have important implications beyond the main effects. The number of unknown parameters in an interaction analysis can be larger or much larger than the sample size. As such, results generated from analyzing a single data set are often unsatisfactory. Integrative analysis, which jointly analyzes the raw data from multiple independent studies, has been conducted in a series of recent studies and shown to outperform single-data set analysis, meta-analysis, and other multi-data set analyses. In this study, our goal is to conduct integrative analysis in interaction analysis. For regularized estimation and selection of important interactions (and main effects), we apply a threshold gradient directed regularization approach. Advancing from the existing studies, the threshold gradient directed regularization approach is modified to respect the "main effects, interactions" hierarchy. The proposed approach has an intuitive formulation and is computationally simple and broadly applicable. Simulations and the analyses of financial early warning system data and news-APP (application) recommendation behavior data demonstrate its satisfactory practical performance.
会议录出版者	WILEY
会议录出版地	111 RIVER ST, HOBOKEN 07030-5774, NJ USA
语种	英语
WOS研究方向	Operations Research & Management Science ; Mathematics
WOS记录号	WOS:000465029700022
内容类型	会议论文
源URL	[http://10.2.47.112/handle/2XS4QKH4/3325]
专题	上海财经大学
作者单位	1.Renmin Univ China, Ctr Appl Stat, Beijing, Peoples R China; 2.Renmin Univ China, Sch Stat, Beijing, Peoples R China; 3.Univ Cincinnati, Operat Business Analyt & Informat Syst Dept, Cincinnati, OH USA; 4.Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai, Peoples R China; 5.Yale Univ, Dept Biostat, New Haven, CT 06510 USA
推荐引用方式 GB/T 7714	Li, Yang,Li, Rong,Qin, Yichen,et al. Integrative interaction analysis using threshold gradient directed regularization[C]. 见:.

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