Open Access
February 2013 Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data
Xuming He, Lan Wang, Hyokyoung Grace Hong
Ann. Statist. 41(1): 342-369 (February 2013). DOI: 10.1214/13-AOS1087


We introduce a quantile-adaptive framework for nonlinear variable screening with high-dimensional heterogeneous data. This framework has two distinctive features: (1) it allows the set of active variables to vary across quantiles, thus making it more flexible to accommodate heterogeneity; (2) it is model-free and avoids the difficult task of specifying the form of a statistical model in a high dimensional space. Our nonlinear independence screening procedure employs spline approximations to model the marginal effects at a quantile level of interest. Under appropriate conditions on the quantile functions without requiring the existence of any moments, the new procedure is shown to enjoy the sure screening property in ultra-high dimensions. Furthermore, the quantile-adaptive framework can naturally handle censored data arising in survival analysis. We prove that the sure screening property remains valid when the response variable is subject to random right censoring. Numerical studies confirm the fine performance of the proposed method for various semiparametric models and its effectiveness to extract quantile-specific information from heteroscedastic data.


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Xuming He. Lan Wang. Hyokyoung Grace Hong. "Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data." Ann. Statist. 41 (1) 342 - 369, February 2013.


Published: February 2013
First available in Project Euclid: 26 March 2013

zbMATH: 1295.62053
MathSciNet: MR3059421
Digital Object Identifier: 10.1214/13-AOS1087

Primary: 62G99 , 68Q32
Secondary: 62E99 , 62N99

Keywords: feature screening , high dimension , polynomial splines , Quantile regression , randomly censored data , sure independence screening

Rights: Copyright © 2013 Institute of Mathematical Statistics

Vol.41 • No. 1 • February 2013
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