We mainly focus on regression estimation in a longitudinal study with nonignorable intermittent nonresponse and dropout.To handle the identifiability issue,we take a time-independent covariate as nonresponse instrumen...We mainly focus on regression estimation in a longitudinal study with nonignorable intermittent nonresponse and dropout.To handle the identifiability issue,we take a time-independent covariate as nonresponse instrument which is independent of nonresponse propensity conditioned on other covariates and responses to ensure the identifiability of nonresponse propensity.The nonresponse propensity is assumed to be a parametric model,and the corresponding parameters are estimated by using the generalized method of moments approach.Then the marginal response means are estimated by inverse probability weighting method.Furthermore,to improve the robustness of estimators,we derive an augmented inverse probability weighting estimator which is shown to be consistent and asymptotically normally distributed.Simulation studies and a real-data analysis show that the proposed approach yields highly efficient estimators.展开更多
基金supported by the National Key Research and Development Plan(No.2016YFC0800100)the NSFC of China(No.11671374,71771203,71631006).
文摘We mainly focus on regression estimation in a longitudinal study with nonignorable intermittent nonresponse and dropout.To handle the identifiability issue,we take a time-independent covariate as nonresponse instrument which is independent of nonresponse propensity conditioned on other covariates and responses to ensure the identifiability of nonresponse propensity.The nonresponse propensity is assumed to be a parametric model,and the corresponding parameters are estimated by using the generalized method of moments approach.Then the marginal response means are estimated by inverse probability weighting method.Furthermore,to improve the robustness of estimators,we derive an augmented inverse probability weighting estimator which is shown to be consistent and asymptotically normally distributed.Simulation studies and a real-data analysis show that the proposed approach yields highly efficient estimators.
