On classification with nonignorable missing data
On classification with nonignorable missing data
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DOI：
10.1016/j.jmva.2021.104755
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发表时间：
2021-03
期刊：
影响因子：
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通讯作者：
M. Mojirsheibani
中科院分区：
文献类型：
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作者：
M. Mojirsheibani
我们考虑了具有不可忽略缺失数据的核分类问题。我们没有对选择概率强加一个对模型假设的违反相当敏感的全参数模型，而是在Kim和Yu(2011)的精神下考虑了一个半参数指数倾斜选择概率模型。除了现有的参数估计器外，我们还发展了一些新的模型未知分量的估计器，这些估计器特别适合于分类问题。我们还研究了所提出的核型分类器的各种强最优性。
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.