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

TL;DR

This paper proposes a flexible, model-free variable screening method for high-dimensional heterogeneous data that adapts across quantiles and can handle censored data, ensuring effective feature selection without specifying a model.

Contribution

It introduces a novel quantile-adaptive, model-free screening framework that accommodates heterogeneity and censored data in high-dimensional settings.

Findings

The method enjoys the sure screening property in ultra-high dimensions.

It effectively handles censored data in survival analysis.

Numerical studies demonstrate superior performance across various models.

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.