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.

在一项纵向研究中，我们主要关注具有不可忽略的间歇性无反应和辍学的回归估计。为了处理可识别性问题，我们采用与时间无关的协变量作为无应答工具，该工具独立于以其他协变量和响应为条件的无应答倾向，以确保无应答倾向的可识别性。假定无响应倾向为参数模型，并使用广义矩量法估算相应的参数。然后通过逆概率加权法估计边际响应均值。此外，为了提高估计器的鲁棒性，我们推导了一个增强的逆概率加权估计器，该估计器被证明是一致的并且渐近正态分布。