Robust feature screening for varying coefficient models via quantile partial correlation

Xiang-Jie Li, Xue-Jun Ma and Jing-Xiao Zhang ()
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Xiang-Jie Li: Renmin University of China
Xue-Jun Ma: College of Applied Sciences Beijing University of Technology
Jing-Xiao Zhang: Renmin University of China

Metrika: International Journal for Theoretical and Applied Statistics, 2017, vol. 80, issue 1, No 2, 17-49

Abstract: Abstract This article is concerned with feature screening for varying coefficient models with ultrahigh-dimensional predictors. We propose a new sure independence screening method based on quantile partial correlation (QPC-SIS), which is quite robust against outliers and heavy-tailed distributions. Then we establish the sure screening property for the QPC-SIS, and conduct simulations to examine its finite sample performance. The results of simulation study indicate that the QPC-SIS performs better than other methods like sure independent screening (SIS), sure independent ranking and screening, distance correlation-sure independent screening, conditional correlation sure independence screening and nonparametric independent screening, which shows the validity and rationality of QPC-SIS.

Keywords: Quantile partial correlation; Ultrahigh-dimensional data; Feature screening; Varying coefficient model (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s00184-016-0589-5

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