Statistical Inference with semiparametric nonignorable nonresponse models

How to deal with nonignorable response is often a challenging problem encountered in statistical analysis with missing data. Parametric model assumption for the response mechanism is sensitive to model misspecification. We consider a semiparametric response model that relaxes the parametric model assumption in the response mechanism. Two types of efficient estimators, profile maximum likelihood estimator and profile calibration estimator, are proposed, and their asymptotic properties are investigated. Two extensive simulation studies are used to compare with some existing methods. We present an application of our method using data from the Korean Labor and Income Panel Survey.