Abstract

Model average techniques are very useful for model-based prediction. However, most earlier works in this field focused on parametric models and continuous responses. In this article, we study varying coefficient multinomial logistic models and propose a semiparametric model averaging prediction (SMAP) approach for multi-category outcomes. The proposed procedure does not need any artificial specification of the index variable in the adopted varying coefficient sub-model structure to forecast the response. In particular, this new SMAP method is more flexible and robust against model misspecification. To improve the practical predictive performance, we combine SMAP with the AdaBoost algorithm to obtain more accurate estimations of class probabilities and model averaging weights. We compare our proposed methods with all existing model averaging approaches and a wide range of popular classification methods via extensive simulations. An automobile classification study is included to illustrate the merits of our methodology. Supplementary materials for this article are available online.

摘要

模型平均技术对于基于模型的预测非常有用。然而，该领域的大多数早期工作都集中在参数模型和连续响应上。在本文中，我们研究了可变系数多项式逻辑模型，并提出了一种用于多类别结果的半参数模型平均预测 (SMAP) 方法。所提出的程序不需要任何人为指定所采用的变系数子模型结构中的指标变量来预测响应。特别是，这种新的 SMAP 方法对模型错误指定更加灵活和鲁棒。为了提高实际预测性能，我们将 SMAP 与 AdaBoost 算法相结合，以获得更准确的类概率估计和模型平均权重。我们通过广泛的模拟将我们提出的方法与所有现有的模型平均方法和广泛的流行分类方法进行比较。包括汽车分类研究以说明我们方法的优点。本文的补充材料可在线获取。