A note on shrinkage sliced inverse regression

• 成果类型: Article
• 署名作者: Ni, LQ; Cook, RD; Tsai, CL
• 署名单位: State University System of Florida; University of Central Florida; University of Minnesota System; University of Minnesota Twin Cities; University of California System; University of California Davis
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/92.1.242
• 发表日期: 2005
• 页码: 242247
• 关键词: principal hessian directions; Dimension Reduction; selection

摘要

We employ Lasso shrinkage within the context of sufficient dimension reduction to obtain a shrinkage sliced inverse regression estimator, which provides easier interpretations and better prediction accuracy without assuming a parametric model. The shrinkage sliced inverse regression approach can be employed for both single-index and multiple-index models. Simulation studies suggest that the new estimator performs well when its tuning parameter is selected by either the Bayesian information criterion or the residual information criterion.