论文标题
在碎片数据预测中平均广义线性模型的模型
Model Averaging for Generalized Linear Models in Fragmentary Data Prediction
论文作者
论文摘要
零碎的数据在许多领域越来越受欢迎，这为研究人员和数据分析师带来了巨大的挑战。涉及碎片数据的大多数现有方法考虑持续响应，而在许多应用程序中，响应变量是离散的。在本文中，我们提出了一种模型平均方法，用于片段数据预测中的通用线性模型。候选模型根据协变量可用性和样本量的不同组合拟合。通过最大程度地减少com的kullback-leibler损失，可以选择最佳权重，并确定其渐近优化性。提出了来自模拟研究的经验证据和有关阿尔茨海默氏病的真实数据分析。
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.
