3 citations · 5 across the 6 of their papers we have counts for
6 papers
Optimal Model Averaging of Support Vector Machines in Diverging Model Spaces
Chaoxia Yuan, Chao Ying, Zhou Yu +1
Support vector machine (SVM) is a powerful classification method that has achieved great success in many fields. Since its performance can be seriously impaired by redundant covari…
A Global Two-stage Algorithm for Non-convex Penalized High-dimensional Linear Regression Problems
Peili Li, Min Liu, Zhou Yu
By the asymptotic oracle property, non-convex penalties represented by minimax concave penalty (MCP) and smoothly clipped absolute deviation (SCAD) have attracted much attentions i…
Fréchet Sufficient Dimension Reduction for Random Objects
Chao Ying, Zhou Yu
We in this paper consider Fréchet sufficient dimension reduction with responses being complex random objects in a metric space and high dimension Euclidean predictors. We propose a…
Distributed estimation of principal support vector machines for sufficient dimension reduction
Jun Jin, Chao Ying, Zhou Yu
The principal support vector machines method (Li et al., 2011) is a powerful tool for sufficient dimension reduction that replaces original predictors with their low-dimensional li…
Dynamic Partial Sufficient Dimension Reduction
Lu Li, Kai Tan, Xuerong Meggie Wen +1
Sufficient dimension reduction aims for reduction of dimensionality of a regression without loss of information by replacing the original predictor with its lower-dimensional subsp…
Overlapping Sliced Inverse Regression for Dimension Reduction
Ning Zhang, Zhou Yu, Qiang Wu
Sliced inverse regression (SIR) is a pioneer tool for supervised dimension reduction. It identifies the effective dimension reduction space, the subspace of significant factors wit…