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
Online-Zugang:	Volltext
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