Estimation and variable selection in generalized partially nonlinear models with nonignorable missing responses

Based on the local kernel estimation method and propensity score adjustment method, we develop a penalized likelihood approach to simultaneously select covariates and explanatory variables in the considered parametric respondent model, and estimate parame

基于局部内核估计方法和倾向得分调整方法，我们开发了一种受惩罚的似然方法，以同时在所考虑的参数受访者模型中选择协变量和解释变量，并估算参数。