Fusion-Refinement Procedure for Dimension Reduction With Missing Response at Random

• 成果类型: Article
• 署名作者: Ding, Xiaobo; Wang, Qihua
• 署名单位: Chinese Academy of Sciences; Academy of Mathematics & System Sciences, CAS; Yunnan University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2011.tm10573
• 发表日期: 2011
• 页码: 1193-1207
• 关键词: sliced inverse regression

摘要

Dimension reduction methods are useful for handling high-dimensional data. It is a common situation that responses of some subjects are not observed in practice. Generally, the missingness carries additional information about the central subspace. Here we propose a two-stage procedure known as the fusion-refinement (FR) procedure. In the first stage, we obtain a subspace including the central subspace by fusing information on regression and missingness. In the second stage, we refine the obtained subspace to recover the central subspace by imputation method. We use sliced inverse regression to illustrate the FR procedure. We conduct simulation studies, and suggest a data-driven procedure to choose from the complete-case analysis and the FR procedure for a purpose of a real application. A real data analysis is used to illustrate our methods.