Factor profiled sure independence screening

H. Wang

Biometrika, 2012, vol. 99, issue 1, 15-28

Abstract: We propose a method of factor profiled sure independence screening for ultrahigh-dimensional variable selection. The objective of this method is to identify nonzero components consistently from a sparse coefficient vector. The new method assumes that the correlation structure of the high-dimensional data can be well represented by a set of low-dimensional latent factors, which can be estimated consistently by eigenvalue-eigenvector decomposition. The estimated latent factors should then be profiled out from both the response and the predictors. Such an operation, referred to as factor profiling, produces uncorrelated predictors. Therefore, sure independence screening can be applied subsequently and the resulting screening result is consistent for model selection, a major advantage that standard sure independence screening does not share. We refer to the new method as factor profiled sure independence screening. Numerical studies confirm its outstanding performance. Copyright 2012, Oxford University Press.

Date: 2012
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