Multiple robustness in factorized likelihood models

• 成果类型: Article
• 署名作者: Molina, J.; Rotnitzky, A.; Sued, M.; Robins, J. M.
• 署名单位: University of Buenos Aires; Universidad Torcuato Di Tella; Harvard University; Harvard T.H. Chan School of Public Health
• 刊物名称: BIOMETRIKA
• ISSN/ISSBN: 0006-3444
• DOI: 10.1093/biomet/asx027
• 发表日期: 2017
• 页码: 561581
• 关键词: Causal Inference; Missing Data; regression-models; repeated outcomes; mean models; EFFICIENCY

摘要

We consider inference under a nonparametric or semiparametric model with likelihood that factorizes as the product of two or more variation-independent factors. We are interested in a finitedimensional parameter that depends on only one of the likelihood factors and whose estimation requires the auxiliary estimation of one or several nuisance functions. We investigate general structures conducive to the construction of so-called multiply robust estimating functions, whose computation requires postulating several dimension-reducing models but which have mean zero at the true parameter value provided one of these models is correct.