C
Cun-Hui Zhang
Researcher at Rutgers University
Publications - 214
Citations - 17085
Cun-Hui Zhang is an academic researcher from Rutgers University. The author has contributed to research in topics: Estimator & Minimax. The author has an hindex of 47, co-authored 212 publications receiving 15041 citations.
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Nearly unbiased variable selection under minimax concave penalty
TL;DR: It is proved that at a universal penalty level, the MC+ has high probability of matching the signs of the unknowns, and thus correct selection, without assuming the strong irrepresentable condition required by the LASSO.
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Nearly unbiased variable selection under minimax concave penalty
TL;DR: In this paper, the authors proposed a penalized linear unbiased selection (PLUS) algorithm, which computes multiple exact local minimizers of a possibly nonconvex penalized loss function in a certain main branch of the graph of critical points of the loss.
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Confidence intervals for low dimensional parameters in high dimensional linear models
Cun-Hui Zhang,Stephanie S. Zhang +1 more
TL;DR: In this article, the authors proposed a method to construct confidence intervals for individual coefficients and linear combinations of several of them in a linear regression model by turning the regression data into an approximate Gaussian sequence of point estimators of individual regression coefficients.
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The sparsity and bias of the Lasso selection in high-dimensional linear regression
Cun-Hui Zhang,Jian Huang +1 more
TL;DR: This article showed that the LASSO selects a model of the correct order of dimensionality, controls the bias of the selected model at a level determined by the contributions of small regression coefficients and threshold bias, and selects all coefficients of greater order than the bias.
Journal ArticleDOI
The sparsity and bias of the Lasso selection in high-dimensional linear regression
Cun-Hui Zhang,Jian Huang +1 more
TL;DR: This article showed that the LASSO selects a model of the correct order of dimensionality, controls the bias of the selected model at a level determined by the contributions of small regression coefficients and threshold bias, and selects all coefficients of greater order than the bias.