## The Annals of Statistics

### Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data

#### Abstract

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.

#### Article information

Source
Ann. Statist., Volume 41, Number 1 (2013), 342-369.

Dates
First available in Project Euclid: 26 March 2013

https://projecteuclid.org/euclid.aos/1364302746

Digital Object Identifier
doi:10.1214/13-AOS1087

Mathematical Reviews number (MathSciNet)
MR3059421

Zentralblatt MATH identifier
1295.62053

#### Citation

He, Xuming; Wang, Lan; Hong, Hyokyoung Grace. Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data. Ann. Statist. 41 (2013), no. 1, 342--369. doi:10.1214/13-AOS1087. https://projecteuclid.org/euclid.aos/1364302746

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#### Supplemental materials

• Supplementary material: “Quantile-adaptive model-free variable screening for high-dimensional heterogeneous data”. We provide additional technical details and numerical examples in the supplemental material.