High-Dimensional Knockoffs Inference for Time Series Data

• 成果类型: Article; Early Access
• 署名作者: Chi, Chien-Ming; Fan, Yingying; Ing, Ching-Kang; Lv, Jinchi
• 署名单位: Academia Sinica - Taiwan; University of Southern California; National Tsing Hua University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2024.2431344
• 发表日期: 2025
• 关键词: false discovery rate; selection; models; Lasso

摘要

We make some initial attempt to establish the theoretical and methodological foundation for the model-X knockoffs inference for time series data. We suggest the method of time series knockoffs inference (TSKI) by exploiting the ideas of subsampling and e-values to address the difficulty caused by the serial dependence. We also generalize the robust knockoffs inference in Barber, Cand & egrave;s, and Samworth to the time series setting to relax the assumption of known covariate distribution required by model-X knockoffs, since such an assumption is overly stringent for time series data. We establish sufficient conditions under which TSKI achieves the asymptotic false discovery rate (FDR) control. Our technical analysis reveals the effects of serial dependence and unknown covariate distribution on the FDR control. We conduct a power analysis of TSKI using the Lasso coefficient difference knockoff statistic under the generalized linear time series models. The finite-sample performance of TSKI is illustrated with several simulation examples and an economic inflation study. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work.