Abstract

In many applied fields incomplete covariate vectors are commonly encountered. It is well known that this can be problematic when making inference on model parameters, but its impact on prediction performance is less understood. We develop a method based on covariate dependent random partition models that seamlessly handles missing covariates while completely avoiding any type of imputation. The method we develop allows in-sample as well as out-of-sample predictions, even if the missing pattern in the new subjects’ incomplete covariate vector was not seen in the training data. Any data type, including categorical or continuous covariates are permitted. In simulation studies, the proposed method compares favorably. We illustrate the method in two application examples. Supplementary materials for this article are available here.

摘要

在许多应用领域中，通常会遇到不完全协变量向量。众所周知，这在对模型参数进行推断时可能会出现问题，但其对预测性能的影响却鲜为人知。我们开发了一种基于协变量相关随机分区模型的方法，该模型可以无缝处理缺失的协变量，同时完全避免任何类型的插补。我们开发的方法允许样本内和样本外的预测，即使在训练数据中没有看到新受试者不完整协变量向量中的缺失模式。允许任何数据类型，包括分类或连续协变量。在模拟研究中，所提出的方法比较有利。我们在两个应用示例中说明了该方法。本文的补充材料可在此处获得。