近期，我院青年教师赵花丽的文章《Augmented transfer regression learning for completely missing covariates》在国际期刊《Journal of the Royal Statistical Society Series B: Statistical Methodology》在线发表。
摘要：Large-scale population-level datasets, such as the UK Biobank and the All of Us Research Program, often lack covariates needed for a specific analysis, such as genetic or lifestyle measures, while related studies measure them. This creates a cross-population missing-data problem in which covariates are completely unobserved in the target population, rather than partially missing within one dataset. We propose an augmented transfer regression learning method for this setting. The key identifying condition is a sub-population shift assumption: the joint distribution of the outcome and observed covariates may differ across source and target populations, but the conditional distribution of the missing covariates given observed variables is invariant. We combine importance-weighted estimating equations with imputation terms for first- and second-order moments of the missing covariates. The resulting estimator is doubly robust and remains consistent if either the density ratio model or both imputation models are correctly specified. It is
-consistent and asymptotically normal and attains the semiparametric efficiency bound when both nuisance models are correctly specified.
论文链接：https://academic.oup.com/jrsssb/advance-article/doi/10.1093/jrsssb/qkag131/8875870?utm_source=authortollfreelink&utm_campaign=jrsssb&utm_medium=email
