Spearman Rank Correlation Screening for Ultrahigh-Dimensional Censored Data
Hongni Wang, Jingxin Yan, Xiaodong Yan
Abstract
Herein, we propose a Spearman rank correlation-based screening procedure for ultrahigh-dimensional data with censored response cases. The proposed method is model-free without specifying any regression forms of predictors or response variables and is robust under the unknown monotone transformations of these response variable and predictors. The sure-screening and rank-consistency properties are established under some mild regularity conditions. Simulation studies demonstrate that the new screening method performs well in the presence of a heavy-tailed distribution, strongly dependent predictors or outliers, and offers superior performance over the existing nonparametric screening procedures. In particular, the new screening method still works well when a response variable is observed under a high censoring rate. An illustrative example is provided.
BibTeX
@article{Wang_Yan_Yan_2023, title={Spearman Rank Correlation Screening for Ultrahigh-Dimensional Censored Data}, volume={37}, url={https://ojs.aaai.org/index.php/AAAI/article/view/26204}, DOI={10.1609/aaai.v37i8.26204}, abstractNote={Herein, we propose a Spearman rank correlation-based screening procedure for ultrahigh-dimensional data with censored response cases. The proposed method is model-free without specifying any regression forms of predictors or response variables and is robust under the unknown monotone transformations of these response variable and predictors. The sure-screening and rank-consistency properties are established under some mild regularity conditions. Simulation studies demonstrate that the new screening method performs well in the presence
of a heavy-tailed distribution, strongly dependent predictors or outliers, and offers superior performance over the existing nonparametric screening procedures. In particular, the new screening method still works well when a response variable is observed under a high censoring rate. An illustrative example is provided.}, number={8}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Wang, Hongni and Yan, Jingxin and Yan, Xiaodong}, year={2023}, month={Jun.}, pages={10104-10112} }