Fragmentary data is becoming more and more popular in many areas which brings big challenges to researchers and data analysts. Most existing methods dealing with fragmentary data consider a continuous response while in many applications the response variable is discrete. In this paper we propose a model averaging method for generalized linear models in fragmentary data prediction. The candidate models are fitted based on different combinations of covariate availability and sample size. The optimal weight is selected by minimizing the Kullback-Leibler loss in the com?pleted cases and its asymptotic optimality is established. Empirical evidences from a simulation study and a real data analysis about Alzheimer disease are presented.

翻译：在许多对研究人员和数据分析员构成重大挑战的领域,碎片数据越来越受欢迎。处理碎片数据的大多数现有方法都考虑到持续的反应,而在许多应用中,反应变量是独立的。在本文件中,我们提出了碎片数据预测中通用线性模型的模型平均法。候选模型是根据共变可用性和样本大小的不同组合而安装的。通过最大限度地减少堆积中Kullback-Libeller的损失及其无药可治的最佳性,选择了最佳的权重。提供了模拟研究和关于阿尔茨海默氏病的实际数据分析的经验证据。