On classification with nonignorable missing data
针对非可忽略缺失数据，采用半参数指数倾斜选择概率模型，提出适用于分类的核分类器新估计量，并证明其强最优性。
We consider the problem of kernel classification with nonignorable missing data. Instead of imposing a fully parametric model for the selection probability, which can be quite sensitive to the violations of model assumptions, here we consider a semiparametric exponential tilting selection probability model in the spirit of Kim and Yu (2011). In addition to the existing parameter estimators, we also develop some new estimators of the unknown components of the model that are particularly suitable for classification problems. We also study various strong optimality properties of the proposed kernel-type classifiers.