Bayesian analysis of longitudinal and multidimensional functional data.
Bayesian analysis of longitudinal and multidimensional functional data.
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纵向和多维函数数据的贝叶斯分析。
DOI：
10.1093/biostatistics/kxaa041
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发表时间：
2022
期刊：
影响因子：
--
通讯作者：
Telesca,Donatello
中科院分区：
文献类型：
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作者：
Shamshoian,John;Şentürk,Damla;Jeste,Shafali;Telesca,Donatello
多维函数数据出现在许多现代科学实验和观察研究中。在本文中，我们关注纵向函数数据，这是多维函数数据的一种结构化形式。在纵向功能框架内操作，我们的目标是捕获低维可解释的功能。我们提出了一种计算效率高的非参数贝叶斯方法，同时平滑观测数据，估计条件函数均值和函数协方差表面。统计推断是基于蒙特卡罗样本的后验测量，通过自适应块吉布斯采样。与建议的建模框架相关联的几个操作特性进行了评估比较，在模拟环境中。我们说明了我们的工作在两个案例研究中的应用。第一个案例研究涉及在不同时间收集的各国按年龄分列的生育率。第二个案例研究是一个自闭症谱系障碍儿童的内隐学习实验。
Multi-dimensional functional data arises in numerous modern scientific experimental and observational studies. In this article, we focus on longitudinal functional data, a structured form of multidimensional functional data. Operating within a longitudinal functional framework we aim to capture low dimensional interpretable features. We propose a computationally efficient nonparametric Bayesian method to simultaneously smooth observed data, estimate conditional functional means and functional covariance surfaces. Statistical inference is based on Monte Carlo samples from the posterior measure through adaptive blocked Gibbs sampling. Several operative characteristics associated with the proposed modeling framework are assessed comparatively in a simulated environment. We illustrate the application of our work in two case studies. The first case study involves age-specific fertility collected over time for various countries. The second case study is an implicit learning experiment in children with autism spectrum disorder.