253 citations · 669 across the 14 of their papers we have counts for
6 papers · 1 filter
Robust and Tuning-Free Sparse Linear Regression via Square-Root Slope
Stanislav Minsker, Mohamed Ndaoud, Lang Wang
We consider the high-dimensional linear regression model and assume that a fraction of the measurements are altered by an adversary with complete knowledge of the data and the unde…
Robust Estimation of Covariance Matrices: Adversarial Contamination and Beyond
Stanislav Minsker, Lang Wang
We consider the problem of estimating the covariance structure of a random vector from a sample . We are interested in the situation when is…
Partially linear additive quantile regression in ultra-high dimension
Ben Sherwood, Lan Wang
We consider a flexible semiparametric quantile regression model for analyzing high dimensional heterogeneous data. This model has several appealing features: (1) By considering dif…
Calibrating nonconvex penalized regression in ultra-high dimension
Lan Wang, Yongdai Kim, Runze Li
We investigate high-dimensional nonconvex penalized regression, where the number of covariates may grow at an exponential rate. Although recent asymptotic theory established that t…
Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data
Xuming He, Lan Wang, Hyokyoung Grace Hong
We introduce a quantile-adaptive framework for nonlinear variable screening with high-dimensional heterogeneous data. This framework has two distinctive features: (1) it allows the…
GEE analysis of clustered binary data with diverging number of covariates
Lan Wang
Clustered binary data with a large number of covariates have become increasingly common in many scientific disciplines. This paper develops an asymptotic theory for generalized est…