Dimension Reduction and Semiparametric Estimation of Survival Models

• 成果类型: Article
• 署名作者: Xia, Yingcun; Zhang, Dixin; Xu, Jinfeng
• 署名单位: National University of Singapore; Nanjing University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/jasa.2009.tm09372
• 发表日期: 2010
• 页码: 278-290
• 关键词: censored regression; selection; squares

摘要

In this paper. we propose a new dimension reduction method by introducing a nominal regression model with the hazard function as the conditional mean. vs Inch naturally retrieves information from complete data and censored data as well Moreover. without requiring the linearity condition, the new method can estimate the entire central subspace consistently and exhaustively The method also provides an alternative approach or the analysis of censored data assuming neither the link function nor the distribution Hence. it exhibits superior robustness properties Numerical studies show that the method can indeed he readily used to efficiently estimate survival node Is. explore the data structures and identity important variables