Locally efficient semiparametric estimators for functional measurement error models

• 成果类型: Article
• 署名作者: Tsiatis, AA; Ma, YY
• 署名单位: North Carolina State University; North Carolina State University
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/91.4.835
• 发表日期: 2004
• 页码: 835848
• 关键词: mixture-models

摘要

A class of semiparametric estimators are proposed in the general setting of functional measurement error models. The estimators follow from estimating equations that are based on the serniparametric efficient score derived under a possibly incorrect distributional assumption for the unobserved 'measured with error' covariates. It is shown that such estimators are consistent and asymptotically normal even with misspecification and are efficient if computed under the truth. The methods are demonstrated with a simulation study of a quadratic logistic regression model with measurement error.