流式纵向数据的统计推断

IF 2.4 2区 数学 Q2 BIOLOGY Biometrika Pub Date : 2023-02-20 DOI:10.1093/biomet/asad010
Lan Luo, Jingshen Wang, Emily C Hector
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引用次数: 0

摘要

现代纵向数据,例如来自可穿戴设备的数据,可能包括在不同数量的时间点对一组固定参与者的生物信号的测量。传统的统计方法无法处理每次收集新数据时重复分析累积增长数据集的计算负担。我们提出了一个新的估计和推理框架,用于动态更新点估计和它们的标准误差,沿顺序收集的数据集内和数据集之间的依赖。该方法的关键技术是将累积纵向数据上构造的二次推理函数的扩展推理函数向量分解为批次数据上的汇总统计和。我们展示了如何在不需要访问整个数据集的情况下递归地更新这个总和,从而在统计效率损失最小的情况下产生计算效率高的流过程。我们证明了流估计器的一致性和渐近正态性,因为数据批次的数量分散,即使独立参与者的数量保持固定。仿真表明,我们的方法优于传统的统计方法,传统的统计方法假设数据批次之间的独立性。最后,我们通过分析国家健康与营养检查调查的加速度计数据,探讨了体育活动与几种疾病之间的关系。
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Statistical inference for streamed longitudinal data
Summary Modern longitudinal data, for example from wearable devices, may consist of measurements of biological signals on a fixed set of participants at a diverging number of time-points. Traditional statistical methods are not equipped to handle the computational burden of repeatedly analysing the cumulatively growing dataset each time new data are collected. We propose a new estimation and inference framework for dynamic updating of point estimates and their standard errors along sequentially collected datasets with dependence, both within and between the datasets. The key technique is a decomposition of the extended inference function vector of the quadratic inference function constructed over the cumulative longitudinal data into a sum of summary statistics over data batches. We show how this sum can be recursively updated without the need to access the whole dataset, resulting in a computationally efficient streaming procedure with minimal loss of statistical efficiency. We prove consistency and asymptotic normality of our streaming estimator as the number of data batches diverges, even as the number of independent participants remains fixed. Simulations demonstrate the advantages of our approach over traditional statistical methods that assume independence between data batches. Finally, we investigate the relationship between physical activity and several diseases through analysis of accelerometry data from the National Health and Nutrition Examination Survey.
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来源期刊
Biometrika
Biometrika 生物-生物学
CiteScore
5.50
自引率
3.70%
发文量
56
审稿时长
6-12 weeks
期刊介绍: Biometrika is primarily a journal of statistics in which emphasis is placed on papers containing original theoretical contributions of direct or potential value in applications. From time to time, papers in bordering fields are also published.
期刊最新文献
Local Bootstrap for Network Data A Simple Bootstrap for Chatterjee's Rank Correlation Sensitivity models and bounds under sequential unmeasured confounding in longitudinal studies Studies in the history of probability and statistics, LI: the first conditional logistic regression Skip-sampling: subsampling in the frequency domain
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