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Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/16132

Title: Conditional sure independence screening
Authors: Barut, Emre
Fan, Jianqing
Issue Date: 2013
Abstract: Independence screening is powerful for variable selection when the number of variables is massive. Commonly used independence screening methods are based on marginal correlations or its variants. When some prior knowledge on a certain important set of variables is available, a natural assessment on the relative importance of the other predictors is their conditional contributions to the response given the known set of variables. This results in conditional sure independence screening (CSIS). CSIS produces a rich family of alternative screening methods by di erent choices of the conditioning set and can help reduce the number of false positive and false negative selections when covariates are highly correlated. This paper proposes and studies CSIS in generalized linear models. 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 and the properties of CSIS when a data-driven conditioning set is used. 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.
URI: http://hdl.handle.net/1942/16132
Category: R2
Type: Research Report
Appears in Collections: Research publications

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