A Model-Free Conditional Screening Approach Via Sufficient Dimension Reduction
Conditional variable screening arises when researchers have prior information regarding the importance of certain predictors. It is natural to consider feature screening methods conditioning on these known important predictors. Barut, E., Fan, J., and Verhasselt, A. [(2016), ‘Conditional Sure Independence Screening’, Journal of the American Statistical Association, 111, 1266-1277] proposed conditional sure independence screening (CSIS) to address this issue under the context of generalised linear models. While CSIS outperforms the marginal screening method when few of the factors are known to be important and/or significant correlations between some of the factors exist, unfortunately, CSIS is model based and might fail when the models are misspecified. We propose a model-free conditional screening method under the framework of sufficient dimension reduction for ultrahigh dimensional statistical problems. Numerical studies show our method easily beats CSIS for nonlinear models and performs comparable to CSIS for (generalised) linear models. Sure screening consistency property for our method is proved.
L. Huo et al., "A Model-Free Conditional Screening Approach Via Sufficient Dimension Reduction," Journal of Nonparametric Statistics, vol. 32, no. 4, pp. 970 - 988, Taylor & Francis, Oct 2020.
The definitive version is available at https://doi.org/10.1080/10485252.2020.1834554
Mathematics and Statistics
Keywords and Phrases
Conditional screening; sufficient dimension reduction; trace pursuit; variable selection
International Standard Serial Number (ISSN)
Article - Journal
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01 Oct 2020