Sequential sufficient dimension reduction for large p, small n problems

• 成果类型: Article
• 署名作者: Yin, Xiangrong; Hilafu, Haileab
• 署名单位: University of Kentucky; University of Tennessee System; University of Tennessee Knoxville
• 刊物名称: JOURNAL OF THE ROYAL STATISTICAL SOCIETY SERIES B-STATISTICAL METHODOLOGY
• ISSN/ISSBN: 1369-7412
• DOI: 10.1111/rssb.12093
• 发表日期: 2015
• 页码: 879-892
• 关键词: sliced inverse regression; variable selection; asymptotic properties; central subspace; CLASSIFICATION; shrinkage

摘要

We propose a new and simple framework for dimension reduction in the large p, small n setting. The framework decomposes the data into pieces, thereby enabling existing approaches for n>p to be adapted to n