A Bayesian functional data model for surveys collected under informative sampling with application to mortality estimation using NHANES
A Bayesian functional data model for surveys collected under informative sampling with application to mortality estimation using NHANES
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DOI：
10.1111/biom.13696
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发表时间：
2020-11
期刊：
影响因子：
1.9
通讯作者：
Paul A. Parker;S. Holan
中科院分区：
文献类型：
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作者：
Paul A. Parker;S. Holan
函数性数据往往是非常高维的，并表现出强烈的相关性结构，但往往被证明对预测和推理都有价值。关于函数数据分析的文献很发达；然而，涉及复杂调查环境中的函数数据的工作很少。基于国家健康和营养检查调查(NHANES)的体力活动监测数据，我们开发了一个能够正确解释调查设计的功能协变量的贝叶斯模型。我们的方法是针对非高斯数据的，并且可以应用于多变量设置。此外，我们利用各种贝叶斯建模技术来确保模型以计算高效的方式进行拟合。我们通过两个模拟研究和一个使用NHANES数据估计死亡率的例子来说明我们方法的价值。
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 two simulation studies as well as an example of mortality estimation using NHANES data.