A unified framework for studying parameter identifiability and estimation in biased sampling designs

• 成果类型: Article
• 署名作者: Chen, Hua Yun
• 署名单位: University of Illinois System; University of Illinois Chicago; University of Illinois Chicago Hospital
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asq059
• 发表日期: 2011
• 页码: 163175
• 关键词: odds ratio; regression-analysis; models; INDEPENDENCE

摘要

Based on the odds ratio representation of a joint density, we propose a unified framework to study parameter identifiability in biased sampling designs. It is shown that most of these designs encountered in practice can be reformulated within the proposed framework and, as a result, the question of parameter identifiability can be largely clarified. Estimation of the identifiable parameters is considered and traditional results on the equivalence of the prospective and retrospective likelihoods are extended. Information contained in data on certain identifiable parameters is often very limited. Such parameters can be poorly estimated by the likelihood approach with practically attainable sample sizes, which can substantially affect the estimates of parameters of primary interest. A partially penalized likelihood approach is proposed to address this. Simulation results suggest that the proposed approach has good performance.