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.

中文翻译：

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关于带有不可忽略的缺失数据的分类

我们考虑具有不可忽略的缺失数据的内核分类问题。与其对选择概率强加一个完全参数化的模型（它可能对模型假设的违背非常敏感），不如在Kim和Yu（2011）的精神下，我们考虑一个半参数指数倾斜选择概率模型。除了现有的参数估计量外，我们还针对模型的未知组件开发了一些新的估计量，这些估计量特别适用于分类问题。我们还研究了提出的核类型分类器的各种强最优性。