文献详情
Integrative interaction analysis using threshold gradient directed regularization
文献类型：会议
年：2019
通讯作者：Wu, MY (reprint author), Shanghai Univ Finance & Econ, Sch Stat & Management, Shanghai, Peoples R China.; Ma, SG (reprint author), Yale Univ, Dept Biostat, New Haven, CT 06510 USA.
会议名称：APPLIED STOCHASTIC MODELS IN BUSINESS AND INDUSTRY
页码范围：354-375
会议开始日期：2019-03-01
所属部门：统计学院
人气指数：2
浏览次数：2
语言：外文
关键词：high-dimensional data; integrative analysis; interaction analysis; TGDR
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, h ...More
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. ...Hide

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