Technological advances associated with data acquisition are leading to the production of complex structured data sets. The recent development on classification with multiclass responses makes it possible to incorporate the dependence structure of predictors. The available methods, however, are hindered by the restrictive requirements. Those methods basically assume a common network structure for predictors of all subjects without taking into account the heterogeneity existing in different classes. Furthermore, those methods mainly focus on the case where the distribution of predictors is normal. In this paper, we propose classification methods which address these limitations. Our methods are flexible in handling possibly class-dependent network structures of variables and allow the predictors to follow a distribution in the exponential family which includes normal distributions as a special case. Our methods are computationally easy to implement. Numerical studies are conducted to demonstrate the satisfactory performance of the proposed methods.

中文翻译：

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网络结构协变量的多类分析和预测

与数据采集相关的技术进步正在导致产生复杂的结构化数据集。具有多类响应的分类的最新发展使得可以合并预测变量的依赖结构。但是，可用的方法受到限制性要求的阻碍。这些方法基本上假定所有对象的预测变量具有相同的网络结构，而不考虑不同类别中存在的异质性。此外，这些方法主要关注预测变量的分布是正态的情况。在本文中，我们提出了解决这些局限性的分类方法。我们的方法可以灵活地处理变量的可能与类相关的网络结构，并允许预测变量遵循指数族中的分布，其中包括正态分布（在特殊情况下）。我们的方法在计算上易于实现。进行了数值研究，以证明所提出方法的令人满意的性能。