Sparse Minimum Discrepancy Approach to Sufficient Dimension Reduction with Simultaneous Variable Selection in Ultrahigh Dimension

2018 ◽  
Vol 114 (527) ◽  
pp. 1277-1290 ◽  
Author(s):  
Wei Qian ◽  
Shanshan Ding ◽  
R. Dennis Cook
Keyword(s):  
Variable Selection ◽  
Dimension Reduction ◽  
Sufficient Dimension Reduction ◽  
Minimum Discrepancy

scholarly journals Coordinate-independent sparse sufficient dimension reduction and variable selection

2010 ◽  
Vol 38 (6) ◽  
pp. 3696-3723 ◽  
Author(s):  
Xin Chen ◽  
Changliang Zou ◽  
R. Dennis Cook
Keyword(s):  
Variable Selection ◽  
Dimension Reduction ◽  
Sufficient Dimension Reduction

scholarly journals Cumulative Median Estimation for Sufficient Dimension Reduction

Stats ◽  
2021 ◽  
Vol 4 (1) ◽  
pp. 138-145
Author(s):  
Stephen Babos ◽  
Andreas Artemiou
Keyword(s):  
Dimension Reduction ◽  
Real Data ◽  
Sufficient Dimension Reduction

In this paper, we present the Cumulative Median Estimation (CUMed) algorithm for robust sufficient dimension reduction. Compared with non-robust competitors, this algorithm performs better when there are outliers present in the data and comparably when outliers are not present. This is demonstrated in simulated and real data experiments.

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