Compared to non-model based classification methods, the model based classification has the advantage of classification together with regression analysis, and is the interest of our investigation. For robustness, we propose and study a semiparametric mixture model, in which each sub-density is only assumed unimodal. The semiparametric maximum likelihood estimate is used to estimate the parametric and nonparametric components. Then the Bayesian classification rule is used to classify the subjects according to the model. Large sample properties of the estimates are investigated, simulation studies are conducted to evaluate the finite sample performance of the proposed model, and then the method is applied to analyze a real data.

中文翻译：

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半参数混合模型的多维分类

与基于非模型的分类方法相比，基于模型的分类具有分类和回归分析的优势，是我们研究的重点。为了提高鲁棒性，我们提出并研究了一个半参数混合模型，其中每个子密度仅假设为单峰。半参数最大似然估计用于估计参数和非参数分量。然后使用贝叶斯分类规则根据模型对主题进行分类。研究了估计的大样本属性，进行了仿真研究以评估所提出模型的有限样本性能，然后将该方法应用于分析实际数据。