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A New Perspective on Robust -Estimation: Finite Sample Theory and Applications to Dependence-Adjusted Multiple Testing
Wen-Xin Zhou, Koushiki Bose, Jianqing Fan +1
Heavy-tailed errors impair the accuracy of the least squares estimate, which can be spoiled by a single grossly outlying observation. As argued in the seminal work of Peter Huber i…
FarmTest: Factor-Adjusted Robust Multiple Testing with Approximate False Discovery Control
Jianqing Fan, Yuan Ke, Qiang Sun +1
Large-scale multiple testing with correlated and heavy-tailed data arises in a wide range of research areas from genomics, medical imaging to finance. Conventional methods for esti…
Generalized High-Dimensional Trace Regression via Nuclear Norm Regularization
Jianqing Fan, Wenyan Gong, Ziwei Zhu
We study the generalized trace regression with a near low-rank regression coefficient matrix, which extends notion of sparsity for regression coefficient vectors. Specifically, giv…
Entrywise Eigenvector Analysis of Random Matrices with Low Expected Rank
Emmanuel Abbe, Jianqing Fan, Kaizheng Wang +1
Recovering low-rank structures via eigenvector perturbation analysis is a common problem in statistical machine learning, such as in factor analysis, community detection, ranking,…
Spectral Method and Regularized MLE Are Both Optimal for Top- Ranking
Yuxin Chen, Jianqing Fan, Cong Ma +1
This paper is concerned with the problem of top- ranking from pairwise comparisons. Given a collection of items and a few pairwise comparisons across them, one wishes to ide…
Adaptive Huber Regression
Qiang Sun, Wenxin Zhou, Jianqing Fan
Big data can easily be contaminated by outliers or contain variables with heavy-tailed distributions, which makes many conventional methods inadequate. To address this challenge, w…