On estimation efficiency of the central mean subspace

• 成果类型: Article
• 署名作者: Ma, Yanyuan; Zhu, Liping
• 署名单位: Texas A&M University System; Texas A&M University College Station; Shanghai University of Finance & Economics
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12044
• 发表日期: 2014
• 页码: 885-901
• 关键词: sliced inverse regression; Dimension Reduction

摘要

We investigate the estimation efficiency of the central mean subspace in the framework of sufficient dimension reduction. We derive the semiparametric efficient score and study its practical applicability. Despite the difficulty caused by the potential high dimension issue in the variance component, we show that locally efficient estimators can be constructed in practice. We conduct simulation studies and a real data analysis to demonstrate the finite sample performance and gain in efficiency of the proposed estimators in comparison with several existing methods.