Academic Report of School of Mathematical Sciences [2026] No. 046

(Series Report for High-Level University Construction No. 1305)

Title： Estimation and inference of high-dimensional factor augmented regression model

Speaker： Xu Guo, Professor (Beijing Normal University)

Time：15:30-16:30, May. 14, 2026

Location：Room 304, Alumni Plaza

Abstract：Factor model is a powerful tool to deal with high correlations among predictors. It has also been incorporated in regression analysis. In this talk, I will share recent developments about estimation and inference of high-dimensional factor augmented regression model. We first address the concern whether it is necessary to consider the augmented part. Existing test procedures do not perform well under dense alternatives. To address this critical issue, we introduce a novel quadratic-type test statistic which can efficiently detect dense alternative hypotheses. We further propose an adaptive test procedure to remain powerful under both sparse and dense alternative hypotheses. We further investigate the penalized estimation of the single-index models with latent factors. With estimated latent factors, we establish the error bounds of the estimators. Lastly, we introduce debiased estimator and construct confidence interval for individual coefficient based on the asymptotic normality. Simulation studies and real data analysis are conducted to illustrate the proposed methods.

Speaker Profile：Xu Guo is currently a professor and doctoral advisor at the School of Statistics, Beijing Normal University. He has been honored as one of the “Top Ten Most Popular Faculty Members Among Undergraduates” at Beijing Normal University (11th edition), and has received the First Prize in the 18th Beijing Normal University Young Teachers’ Teaching Competition and the Third Prize in the 13th Beijing Municipal Young Teachers’ Teaching Competition. His current research focuses on hypothesis testing in high-dimensional regression models, and he is also interested in statistical inference based on machine learning algorithms. He has published numerous papers in leading international journals in statistics and econometrics, including the Journal of the Royal Statistical Society: Series B (JRSSB), the Journal of the American Statistical Association (JASA), Biometrika, the Journal of Operational Economics (JOE), and the Journal of the Mathematical Laboratory of the Royal Society (JMLR). He serves as an Associate Editor for the internationally renowned statistics journal Journal of Mathematical and Variational Analysis (JMVA).

Faculty and students are welcome to attend!

Invited by: Jiangzhou Wang

School of Mathematical Sciences

May 11, 2026