7 citations · 28 across the 11 of their papers we have counts for
12 papers
A Unified Algorithm for Penalized Convolution Smoothed Quantile Regression
Rebeka Man, Xiaoou Pan, Kean Ming Tan +1
Penalized quantile regression (QR) is widely used for studying the relationship between a response variable and a set of predictors under data heterogeneity in high-dimensional set…
High-Dimensional Quantile Regression: Convolution Smoothing and Concave Regularization
Kean Ming Tan, Lan Wang, Wen-Xin Zhou
-penalized quantile regression is widely used for analyzing high-dimensional data with heterogeneity. It is now recognized that the -penalty introduces non-negligib…
Estimating and Inferring the Maximum Degree of Stimulus-Locked Time-Varying Brain Connectivity Networks
Kean Ming Tan, Junwei Lu, Tong Zhang +1
Neuroscientists have enjoyed much success in understanding brain functions by constructing brain connectivity networks using data collected under highly controlled experimental set…
Resistant convex clustering: How does the fusion penalty enhance resistantance?
Qiang Sun, Archer Gong Zhang, Chenyu Liu +1
Convex clustering is a convex relaxation of the -means and hierarchical clustering. It involves solving a convex optimization problem with the objective function being a squared…
Robust Sparse Reduced Rank Regression in High Dimensions
Kean Ming Tan, Qiang Sun, Daniela Witten
We propose robust sparse reduced rank regression for analyzing large and complex high-dimensional data with heavy-tailed random noise. The proposed method is based on a convex rela…
A convex formulation for high-dimensional sparse sliced inverse regression
Kean Ming Tan, Zhaoran Wang, Tong Zhang +2
Sliced inverse regression is a popular tool for sufficient dimension reduction, which replaces covariates with a minimal set of their linear combinations without loss of informatio…