Inference for a class of transformed hazards models

• 成果类型: Article
• 署名作者: Zeng, DL; Yin, GS; Ibrahim, JG
• 署名单位: University of North Carolina; University of North Carolina Chapel Hill; University of Texas System; UTMD Anderson Cancer Center
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1198/016214504000001637
• 发表日期: 2005
• 页码: 1000-1008
• 关键词: SEMIPARAMETRIC ANALYSIS; regression

摘要

A new class of transformed hazard rate models is considered that contains both the multiplicative hazards model and the additive hazards model as special cases. The sieve maximum likelihood estimators are derived for the model parameters, and the estimators for the regression coefficients are shown to be consistent and asymptotically normal with variance achieving the semiparametric efficiency bound. Simulation studies are conducted to examine the small-sample properties of the proposed estimates, and a real dataset is used to illustrate our approach.