Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
7546856 | Journal of Multivariate Analysis | 2016 | 48 Pages |
Abstract
For screening features in an ultrahigh-dimensional setting, we develop a maximum correlation-based sure independence screening (MC-SIS) procedure, and show that MC-SIS possesses the sure screen property without imposing model or distributional assumptions on the response and predictor variables. MC-SIS is a model-free method in contrast with some other existing model-based sure independence screening methods in the literature. Simulation examples and a real data application are used to demonstrate the performance of MC-SIS and to compare MC-SIS with other existing sure screening methods. The results show that MC-SIS can outperform those methods when their model assumptions are violated, and remain competitive when the model assumptions are satisfied.
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Numerical Analysis
Authors
Qiming Huang, Yu Zhu,