90 citations · 112 across the 8 of their papers we have counts for
8 papers
A selective review of sufficient dimension reduction for multivariate response regression
Yuexiao Dong, Abdul-Nasah Soale, Michael D. Power
We review sufficient dimension reduction (SDR) estimators with multivariate response in this paper. A wide range of SDR methods are characterized as inverse regression SDR estimato…
On sufficient dimension reduction via principal asymmetric least squares
Abdul-Nasah Soale, Yuexiao Dong
In this paper, we introduce principal asymmetric least squares (PALS) as a unified framework for linear and nonlinear sufficient dimension reduction. Classical methods such as slic…
On expectile-assisted inverse regression estimation for sufficient dimension reduction
Abdul-Nasah Soale, Yuexiao Dong
Moment-based sufficient dimension reduction methods such as sliced inverse regression may not work well in the presence of heteroscedasticity. We propose to first estimate the expe…
Sparse Representation Classification via Screening for Graphs
Cencheng Shen, Li Chen, Yuexiao Dong +1
The sparse representation classifier (SRC) is shown to work well for image recognition problems that satisfy a subspace assumption. In this paper we propose a new implementation of…
High-dimensional Interactions Detection with Sparse Principal Hessian Matrix
Cheng Yong Tang, Ethan X. Fang, Yuexiao Dong
In statistical learning framework with regressions, interactions are the contributions to the response variable from the products of the explanatory variables. In high-dimensional…
Sparse Representation Classification Beyond L1 Minimization and the Subspace Assumption
Cencheng Shen, Li Chen, Yuexiao Dong +1
The sparse representation classifier (SRC) has been utilized in various classification problems, which makes use of L1 minimization and works well for image recognition satisfying…
Trace Pursuit: A General Framework for Model-Free Variable Selection
Zhou Yu, Yuexiao Dong, Li-Xing Zhu
We propose trace pursuit for model-free variable selection under the sufficient dimension reduction paradigm. Two distinct algorithms are proposed: stepwise trace pursuit and forwa…
Dimension reduction for nonelliptically distributed predictors
Bing Li, Yuexiao Dong
Sufficient dimension reduction methods often require stringent conditions on the joint distribution of the predictor, or, when such conditions are not satisfied, rely on marginal t…