Feature screening of quadratic inference functions for ultrahigh dimensional longitudinal data

Abstract

This paper is concerned with feature screening for the ultrahigh dimensional additive models with longitudinal data. The proposed method utilizes the quadratic inference functions to construct the marginal screening measurement, which takes the within-subject correlation into consideration and is more efficient and robust than some parametric model assumptions for the working covariance matrix in each subject or experimental unit. We also show that the proposed method enjoys the sure screening property under some regularity conditions. Monte Carlo simulation studies and a real data application are conducted to examine the performance of the proposed method.

Keywords:

• Quadratic inference function
• ultrahigh dimensional data
• longitudinal data
• feature screening
• sure screening property

2010 Mathematics Subject Classification:

• 62G99

Acknowledgments

We thank the associate editor and reviewers for their careful review and insightful comments, which have led to a significant improvement of this article. This study was supported by National Natural Science Foundation of China (11771215, 11971404), Natural Science Foundation of Jiangsu Province (BK20161530), Qing Lan Project of Jiangsu Province (2016), MOE (Ministry of Education in China) Project of Humanities and Social Sciences (19YJC910010).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This study was supported by National Natural Science Foundation of China [grant numbers 11771215, 11971404], Natural Science Foundation of Jiangsu Province [grant number BK20161530], Qing Lan Project of Jiangsu Province (2016), MOE (Ministry of Education in China) Project of Humanities and Social Sciences [grant number 19YJC910010].