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arXiv · 1702.02708

Spearman Rank Correlation Screening for Ultrahigh-dimensional Censored Data

Hongni Wang·Jingxin Yan·Xiaodong Yan
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Abstract

Herein, we propose a Spearman rank correlation based screening procedure for ultrahigh-dimensional data with censored response case. The proposed method is model-free without specifying any regression forms of predictors or response variable 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 that 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.

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Hongni Wang, Jingxin Yan, Xiaodong Yan. 2017-02-09. Spearman Rank Correlation Screening for Ultrahigh-dimensional Censored Data. https://arxiv.org/abs/1702.02708

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