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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Veröffentlicht in:	Journal of the American Statistical Association Jg. 106; H. 496; S. 1464 - 1475
Hauptverfasser:	Zhu, Li-Ping, Li, Lexin, Li, Runze, Zhu, Li-Xing
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Alexandria, VA Taylor & Francis 01.12.2011 American Statistical Association Taylor & Francis Ltd
Schlagworte:	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 Rank statistic Data analysis Sample size Explosions Parametric model Semiparametric model Parametric method Statistical method Statistical regression Selection problem Sampling theory Simulation Regression model Ultrahigh-dimensional regression Sample survey Feature ranking Variable selection
ISSN:	0162-1459, 1537-274X
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Beschreibung
Zusammenfassung:	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.
Bibliographie:	SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 14 ObjectType-Article-2 content type line 23 ObjectType-Article-1 ObjectType-Feature-2
ISSN:	0162-1459 1537-274X
DOI:	10.1198/jasa.2011.tm10563