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Abstract:
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Feature screening plays an important role in dimension reduction for ultrahigh-dimensional data. In this talk, we introduce a new feature screening method and establishes its sure independence screening property under the ultrahigh-dimensional setting. The proposed method works based on the nonparanormal transformation and Henze-Zirkler's test, which is to first transform the response variable and features to Gaussian random variables using the nonparanormal transformation and then test the dependence between the response variable and features using the Henze-Zirkler's test. The proposed method enjoys at least two merits. First, it is model-free, which avoids the specifi cation for a particular model structure. Second, it is condition-free, which does not require any extra conditions except for some regularity conditions for high-dimensional feature screening. The numerical results indicate that, compared to the existing methods, the proposed method is more robust to the data generated from heavy-tailed distributions and/or complex models with interaction variables. The proposed method is applied to screening of anticancer drug response genes.
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