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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…
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,…
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…