A Unified Approach to Semiparametric Transformation Models Under General Biased Sampling Schemes

• 成果类型: Article
• 署名作者: Kim, Jane Paik; Lu, Wenbin; Sit, Tony; Ying, Zhiliang
• 署名单位: Stanford University; North Carolina State University; Columbia University
• 刊物名称: JOURNAL OF THE AMERICAN STATISTICAL ASSOCIATION
• ISSN/ISSBN: 0162-1459
• DOI: 10.1080/01621459.2012.746073
• 发表日期: 2013
• 页码: 217-227
• 关键词: case-cohort; nonparametric-estimation; empirical distributions; DENSITY-ESTIMATION; regression-models; likelihood; EFFICIENCY

摘要

We propose a unified estimation method for semiparametric linear transformation models under general biased sampling schemes. The new estimator is obtained from a set of counting process-based unbiased estimating equations, developed through introducing a general weighting scheme that offsets the sampling bias. The usual asymptotic properties, including consistency and asymptotic normality, are established under suitable regularity conditions. A closed-form formula is derived for the limiting variance and the plug-in estimator is shown to be consistent. We demonstrate the unified approach through the special cases of left truncation, length bias, the case-cohort design, and variants thereof. Simulation studies and applications to real datasets are presented.