Trace Pursuit: A General Framework for Model-Free Variable Selection

• 成果类型: Article
• 署名作者: Yu, Zhou; Dong, Yuexiao; Zhu, Li-Xing
• 署名单位: Beijing Normal University; Hong Kong Baptist University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2015.1050494
• 发表日期: 2016
• 页码: 813-821
• 关键词: sliced inverse regression; Dimension Reduction; likelihood; shrinkage; rank

摘要

We propose trace pursuit for model-free variable selection under the sufficient dimension-reduction paradigm. Two distinct algorithms are proposed: stepwise trace pursuit and forward trace pursuit. Stepwise trace pursuit achieves selection consistency with fixed p. Forward trace pursuit can serve as an initial screening step to speed up the computation in the case of ultrahigh dimensionality. The screening, consistency property of forward trace pursuit based on sliced inverse regression is established. Finite sample performances of trace pursuit and other model-free variable selection methods are compared through numerical studies. Supplementary materials for this article are available online.