Model-Free Feature Screening for Ultrahigh-Dimensional Data

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...

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Bibliographic Details
Published in:Journal of the American Statistical Association 2011-12, Vol.106 (496), p.1464-1475
Main Authors: Zhu, Li-Ping, Li, Lexin, Li, Runze, Zhu, Li-Xing
Format: Article
Language:English
Subjects:
Applications
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
Regression analysis
Sample size
Sampling techniques
Sciences and techniques of general use
Statistical models
Statistics
Theory and Methods
Threshing
Ultrahigh-dimensional regression
Variable selection
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Summary: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.
ISSN:0162-1459
1537-274X
DOI:10.1198/jasa.2011.tm10563