Transformed sufficient dimension reduction

• 成果类型: Article
• 署名作者: Wang, T.; Guo, X.; Zhu, L.; Xu, P.
• 署名单位: Hong Kong Baptist University; Southeast University - China
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asu037
• 发表日期: 2014
• 页码: 815829
• 关键词: sliced inverse regression

摘要

We propose a general framework for dimension reduction in regression to fill the gap between linear and fully nonlinear dimension reduction. The main idea is to first transform each of the raw predictors monotonically and then search for a low-dimensional projection in the space defined by the transformed variables. Both user-specified and data-driven transformations are suggested. In each case, the methodology is first discussed in generality and then a representative method is proposed and evaluated by simulation. The proposed methods are applied to a real dataset.