Empirical Likelihood-Based Inference for Functional Means with Application to Wearable Device Data
Empirical Likelihood-Based Inference for Functional Means with Application to Wearable Device Data
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基于经验似然的函数方法推理及其在可穿戴设备数据中的应用
DOI：
10.1111/rssb.12543
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发表时间：
2022
期刊：
影响因子：
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通讯作者：
McKeague, Ian W.
中科院分区：
文献类型：
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作者：
Chang, Hsin-wen;McKeague, Ian W.
提出了一种适用于从可穿戴设备数据中提取占用时间曲线的非参数推理框架。这些曲线考虑了设备读数范围内的所有活动水平，这比将活动分类为离散类别的做法更可取。受这些曲线的某些特征的启发，我们引入了一种强大的似然比方法来构造置信带和比较函数平均。值得注意的是，我们的方法允许在功能协方差中不连续，同时适应观察到的轨迹的离散化。一项模拟研究表明，所提出的方法优于竞争的函数数据方法。我们使用来自NHANES研究的可穿戴设备数据来说明所提出的方法。
This paper develops a nonparametric inference framework that is applicable to occupation time curves derived from wearable device data. These curves consider all activity levels within the range of device readings, which is preferable to the practice of classifying activity into discrete categories. Motivated by certain features of these curves, we introduce a powerful likelihood ratio approach to construct confidence bands and compare functional means. Notably, our approach allows discontinuities in the functional covariances while accommodating discretization of the observed trajectories. A simulation study shows that the proposed procedures outperform competing functional data procedures. We illustrate the proposed methods using wearable device data from an NHANES study.