TWO-SAMPLE TESTS FOR MULTIVARIATE REPEATED MEASUREMENTS OF HISTOGRAM OBJECTS WITH APPLICATIONS TO WEARABLE DEVICE DATA.
TWO-SAMPLE TESTS FOR MULTIVARIATE REPEATED MEASUREMENTS OF HISTOGRAM OBJECTS WITH APPLICATIONS TO WEARABLE DEVICE DATA.
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DOI：
10.1214/21-aoas1596
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发表时间：
2022-12
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影响因子：
--
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中科院分区：
文献类型：
--
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重复观察在生物医学研究和纵向研究中越来越常见。例如，可穿戴传感器设备被部署为在多天内连续跟踪来自每个个体的生理和生物信号。适当评估生物信号的每日分布在疾病组和人口统计学中的差异仍然非常有趣。因此，这些数据可以被公式化为多变量复杂对象数据，例如概率密度、直方图和树上的观察。传统的统计方法通常无法应用，因为它们是从任意的非欧几里德度量空间中采样的。在本文中，我们提出了新的，非参数的，基于图形的两个样本测试的对象数据具有相同的结构的重复测量。我们将重复测量的对象数据视为多变量对象数据，这需要每个个体相同数量的重复观测，但消除了对重复观测误差的任何假设。一组测试统计量，提出了捕捉各种可能的替代品。在零置换下，我们得到了它们的渐近零分布。这些测试表现出显着的权力改善现有的方法，同时控制有限样本下的I型错误，通过模拟研究所示。所提出的测试证明，以提供额外的见解的位置，个体间和个体内的日常身体活动分布的样本中的情绪障碍的研究。
Repeated observations have become increasingly common in biomedical research and longitudinal studies. For instance, wearable sensor devices are deployed to continuously track physiological and biological signals from each individual over multiple days. It remains of great interest to appropriately evaluate how the daily distribution of biosignals might differ across disease groups and demographics. Hence, these data could be formulated as multivariate complex object data, such as probability densities, histograms, and observations on a tree. Traditional statistical methods would often fail to apply, as they are sampled from an arbitrary non-Euclidean metric space. In this paper we propose novel, nonparametric, graph-based two-sample tests for object data with the same structure of repeated measures. We treat the repeatedly measured object data as multivariate object data, which requires the same number of repeated observations per individual but eliminates any assumptions on the errors of the repeated observations. A set of test statistics are proposed to capture various possible alternatives. We derive their asymptotic null distributions under the permutation null. These tests exhibit substantial power improvements over the existing methods while controlling the type I errors under finite samples as shown through simulation studies. The proposed tests are demonstrated to provide additional insights on the location, inter- and intra-individual variability of the daily physical activity distributions in a sample of studies for mood disorders.
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影响因子：
1.9
作者：
DAWSON, JD;LAGAKOS, SW
通讯作者：
LAGAKOS, SW
影响因子：
4.5
作者：
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Kriemler, Susi
影响因子：
4.3
作者：
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Hickie, Ian B.
影响因子：
3.7
作者：
SCHILLING, MF
通讯作者：
SCHILLING, MF
影响因子：
1.8
作者：
Schott, James R.
通讯作者：
Schott, James R.