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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/19102

Title: Conditional sure independence screening
Authors: Barut, Emre
Fan, Jianqing
Verhasselt, Anneleen
Issue Date: 2015
Citation: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION 111 (515), p. 1266-1277.
Abstract: Independence screening is a powerful method for variable selection for ‘Big Data’ when the number of variables is massive. Commonly used independence screening methods are based on marginal correlations or variations of it. In many applications, researchers often have some prior knowledge that a certain set of variables is related to the response. In such a situation, a natural assessment on the relative importance of the other predictors is the conditional contributions of the individual predictors in presence of the known set of variables. This results in conditional sure independence screening (CSIS). Conditioning helps for reducing the false positive and the false negative rates in the variable selection process. In this paper, we propose and study CSIS in the context of generalized linear models. For ultrahigh-dimensional statistical problems, we give conditions under which sure screening is possible and derive an upper bound on the number of selected variables. We also spell out the situation under which CSIS yields model selection consistency. Moreover, we provide two data-driven methods to select the thresholding parameter of conditional screening. The utility of the procedure is illustrated by simulation studies and analysis of two real data sets.
Notes: Barut, E (reprint author), George Washington Univ, Dept Stat, Washington, DC 20052 USA. barut@gwu.edu
URI: http://hdl.handle.net/1942/19102
Link to publication: http://europepmc.org/backend/ptpmcrender.fcgi?accid=PMC5367860&blobtype=pdf
DOI: 10.1080/01621459.2015.1092974
ISI #: 000386318200032
ISSN: 0162-1459
Category: A1
Type: Journal Contribution
Validation: ecoom, 2017
Appears in Collections: Research publications

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