Author

Meng-Tao Wen

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Preprint Oct 2026

Controlling the False Discovery Rate via Model-Agnostic Importance Measures

Selecting covariates while controlling the false discovery rate (FDR) is critical for interpretable scientific discovery, especially with complex machine learning models. Traditional variable selection methods often rely on specific model assumptions, while Model-X knockoffs provide a general way that leverages knowled...

Meng-Tao Wen, Hao-Jie Ren, Chang-Liang Zou · 0 citations

Preprint Sep 2026

Two-sample tests for principal eigenvalues and eigenvectors in high-dimensional elliptical factor models

Changes in principal eigenvalues and eigendirections provide complementary diagnostics of structural instability in factor models, but covariance-based inference can be unreliable under heavy tails. We develop two-sample tests for equality of these features of trace-normalized shape matrices under high-dimensional elli...

Xin-Yue Xu, Meng-Tao Wen, Long Feng · 0 citations