Journal Article

Model-Free Feature Screening for Ultrahigh-Dimensional Data

Li, Runze, Zhu, Li-Ping, Li, Lexin, Zhu, Li-Xing

2011

Overview

With the recent explosion of scientific data of unprecedented size and complexity, feature ranking and screening are playing an increasingly important role in many scientific studies. In this article, we propose a novel feature screening procedure under a unified model framework, which covers a wide variety of commonly used parametric and semiparametric models. The new method does not require imposing a specific model structure on regression functions, and thus is particularly appealing to ultrahigh-dimensional regressions, where there are a huge number of candidate predictors but little information about the actual model forms. We demonstrate that, with the number of predictors growing at an exponential rate of the sample size, the proposed procedure possesses consistency in ranking, which is both useful in its own right and can lead to consistency in selection. The new procedure is computationally efficient and simple, and exhibits a competent empirical performance in our intensive simulations and real data analysis.

• Publisher: Taylor & Francis,American Statistical Association,Taylor & Francis Ltd
• Subject: Applications / Candidates / Computational methods / Data analysis / Distribution / Exact sciences and technology / Feature ranking / Framing / Gaussian distributions / General topics / Genetic screening / Grants / Linear inference, regression / Linear models / Linear regression / Mathematics / Medical procedures / Medical screening / Methodology / Modeling / Parameter estimation / Parametric inference / Probability and statistics / Ratings & rankings / Regression analysis / Sample size / Sampling techniques / Sciences and techniques of general use / Statistical models / Statistics / Tests / Theory and Methods / Threshing / Ultrahigh-dimensional regression / Variable selection