Integrative interaction analysis using threshold gradient directed regularization

Yang Li, Rong Li, Yichen Qin, Mengyun Wu and Shuangge Ma

Applied Stochastic Models in Business and Industry, 2019, vol. 35, issue 2, 354-375

Abstract: 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.

Date: 2019
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://doi.org/10.1002/asmb.2342

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:apsmbi:v:35:y:2019:i:2:p:354-375

Access Statistics for this article

More articles in Applied Stochastic Models in Business and Industry from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().