Robust and sparse learning of varying coefficient models with high-dimensional features

Wei Xiong, Maozai Tian, Man Lai Tang, Han Pang

Research output: Contribution to journalArticlepeer-review


Varying coefficient model (VCM) is extensively used in various scientific fields due to its capability of capturing the changing structure of predictors. Classical mean regression analysis is often complicated in the existence of skewed, heterogeneous and heavy-tailed data. For this purpose, this work employs the idea of model averaging and introduces a novel comprehensive approach by incorporating quantile-adaptive weights across different quantile levels to further improve both least square (LS) and quantile regression (QR) methods. The proposed procedure that adaptively takes advantage of the heterogeneous and sparse nature of input data can gain more efficiency and be well adapted to extreme event case and high-dimensional setting. Motivated by its nice properties, we develop several robust methods to reveal the dynamic close-to-truth structure for VCM and consistently uncover the zero and nonzero patterns in high-dimensional scientific discoveries. We provide a new iterative algorithm that is proven to be asymptotic consistent and can attain the optimal nonparametric convergence rate given regular conditions. These introduced procedures are highlighted with extensive simulation examples and several real data analyses to further show their stronger predictive power compared with LS, composite quantile regression (CQR) and QR methods.
Original languageEnglish
Pages (from-to)3312 - 3336
JournalJournal of Applied Statistics
Issue number16
Publication statusPublished - 9 Aug 2022


Dive into the research topics of 'Robust and sparse learning of varying coefficient models with high-dimensional features'. Together they form a unique fingerprint.

Cite this