Robust feature screening for varying coefficient models via quantile partial correlation

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 e...

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Bibliographic Details
Published in:Metrika 2017, Vol.80 (1), p.17-49
Main Authors: Li, Xiang-Jie, Ma, Xue-Jun, Zhang, Jing-Xiao
Format: Article
Language:English
Subjects:
Computer simulation
Correlation
Economic Theory/Quantitative Economics/Mathematical Methods
Mathematics and Statistics
Monte Carlo simulation
Probability Theory and Stochastic Processes
Regression analysis
Screening
Statistics
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Summary: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.
ISSN:0026-1335
1435-926X
DOI:10.1007/s00184-016-0589-5