Model-free sure screening via maximum correlation

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• 作者：Qiming Huang ; ^{hqm@purdue.edu" class="auth_mail" title="E-mail the corresponding author} ; Yu Zhu ^{yuzhu@purdue.edu" class="auth_mail" title="E-mail the corresponding author}
• 关键词：primary ; 62J99 ; secondary ; 62F12
• 刊名：Journal of Multivariate Analysis
• 出版年：2016
• 出版时间：June 2016
• 年：2016
• 卷：148
• 期：Complete
• 页码：89-106
• 全文大小：453 K

文摘

For screening features in an ultrahigh-dimensional setting, we develop a maximum correlation-based sure independence screening (MC-SIS) procedure, and show that MC-SIS possesses the sure screen property without imposing model or distributional assumptions on the response and predictor variables. MC-SIS is a model-free method in contrast with some other existing model-based sure independence screening methods in the literature. Simulation examples and a real data application are used to demonstrate the performance of MC-SIS and to compare MC-SIS with other existing sure screening methods. The results show that MC-SIS can outperform those methods when their model assumptions are violated, and remain competitive when the model assumptions are satisfied.