High-Dimensional Interaction Detection With False Sign Rate Control
Bibliographic Data
| ID | 19420145 |
|---|---|
| Authors | Daoji Li (0000-0002-3447-3485, Department of Information Systems and Decision Sciences, College of Business and Economics, California State University, Fullerton, CA, corresponding author), Yinfei Kong (0000-0002-3298-8020, Department of Information Systems and Decision Sciences, College of Business and Economics, California State University, Fullerton, CA), Yingying Fan (0000-0003-1726-4654, University of Southern California), Jinchi Lv (0000-0002-5881-9591, University of Southern California) |
| Year | 2022 |
| Volume | 40 |
| Issue | 3 |
| Pages | 1234-1245 |
| Publication date | 2022-07-03 |
| Open Access | No |
| Type | ARTICLE |
| Venue | Journal of Business and Economic Statistics (JOURNAL) |
| Journal identifiers | ISSN: 0735-0015 • E-ISSN: 1537-2707 |
| Publisher | Informa UK Limited (PUBLISHER • GB) |
| DOI | 10.1080/07350015.2021.1917419 |
| OpenAlex | W3152682795 |
| Language | EN |
| References cited | 31 |
Identifying interaction effects is fundamentally important in many scientific discoveries and contemporary applications, but it is challenging since the number of pairwise interactions increases quadratically with the number of covariates and that of higher-order interactions grows even faster. Although there is a growing literature on interaction detection, little work has been done on the prediction and false sign rate on interaction detection in ultrahigh-dimensional regression models. This article fills such a gap. More specifically, in this article we establish some theoretical results on interaction selection for ultrahigh-dimensional quadratic regression models under random designs. We prove that the examined method enjoys the same oracle inequalities as the lasso estimator and further admits an explicit bound on the false sign rate. Moreover, the false sign rate can be asymptotically vanishing. These new theoretical characterizations are confirmed by simulation studies. The performance of our proposed approach is further illustrated through a real data application
Algorithm · Covariate · Econometrics · Estimator · Model selection · Multiple comparisons problem · Oracle · Pairwise comparison · Quadratic equation · Quadratic growth · Regression · Statistics · Computer Science · Mathematics · Optimal Experimental Design Methods · Probabilistic and Robust Engineering Design · Statistical Methods and Inference · Applied Mathematics
| Highly cited | No |
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