@article{zhang_wang_wu_2018, title={Functional envelope for model-free sufficient dimension reduction}, volume={163}, journal={Journal of Multivariate Analysis}, author={Zhang, X. and Wang, C. and Wu, Y. C.}, year={2018}, pages={37–50} }
 @article{wang_shin_wu_2018, title={Principal quantile regression for sufficient dimension reduction with heteroscedasticity}, volume={12}, ISSN={["1935-7524"]}, DOI={10.1214/18-EJS1432}, abstractNote={: Sufficient dimension reduction (SDR) is a successful tool for re- ducing data dimensionality without stringent model assumptions. In practice, data often display heteroscedasticity which is of scientific importance in general but frequently overlooked since a primal goal of most existing statistical methods is to identify conditional mean relationship among variables. In this article, we propose a new SDR method called principal quantile regression (PQR) that efficiently tackles heteroscedasticity. PQR can naturally be extended to a nonlinear version via kernel trick. Asymptotic properties are established and an efficient solution path-based algo- rithm is provided. Numerical examples based on both simulated and real data demonstrate the PQR’s advantageous performance over existing SDR methods. PQR still performs very competitively even for the case without heteroscedasticity.}, number={2}, journal={ELECTRONIC JOURNAL OF STATISTICS}, author={Wang, Chong and Shin, Seung Jun and Wu, Yichao}, year={2018}, pages={2114–2140} }