论文标题
在信息抽样下收集的调查的贝叶斯功能数据模型，并应用了NHANES死亡率估算
A Bayesian Functional Data Model for Surveys Collected under Informative Sampling with Application to Mortality Estimation using NHANES
论文作者
论文摘要
功能数据通常是极高的，并且表现出强大的依赖性结构，但通常可以证明对预测和推理都有价值。有关功能数据分析的文献良好；但是，在复杂的调查设置中涉及功能数据的工作很少。由体育活动监测到国家健康和营养检查调查（NHANES）的数据，我们开发了一种用于功能协变量的贝叶斯模型，该模型可以正确考虑调查设计。我们的方法用于非高斯数据，可以应用于多元设置。此外，我们还利用各种贝叶斯建模技术来确保模型以计算有效的方式拟合。我们通过经验模拟研究来说明方法的价值，以及使用NHANES数据估算死亡率的示例。
Functional data are often extremely high-dimensional and exhibit strong dependence structures but can often prove valuable for both prediction and inference. The literature on functional data analysis is well developed; however, there has been very little work involving functional data in complex survey settings. Motivated by physical activity monitor data from the National Health and Nutrition Examination Survey (NHANES), we develop a Bayesian model for functional covariates that can properly account for the survey design. Our approach is intended for non-Gaussian data and can be applied in multivariate settings. In addition, we make use of a variety of Bayesian modeling techniques to ensure that the model is fit in a computationally efficient manner. We illustrate the value of our approach through an empirical simulation study as well as an example of mortality estimation using NHANES data.
