Addressing zero-inflated and mis-measured functional predictors in scalar-on-function regression model
标量对函数回归中零膨胀与测量误差的函数型预测变量处理AI 解读 ↓
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标量对函数回归中零膨胀与测量误差的函数型预测变量处理
- 研究问题
- 摘要称，研究在多层级标量对函数回归模型中，函数型预测变量同时存在过量零值与测量误差时，如何校正由此产生的估计与推断偏误。
- 方法与思路
- 作者提出半连续建模方法，用于校正零膨胀与测量误差带来的偏误；摘要称给出了理论依据，并通过大量模拟展示有限样本性质，最后应用于一项学校干预研究。
- 材料陈述的贡献
- 摘要明确陈述的贡献是：提出可同时处理零膨胀与测量误差的半连续建模方法，提供理论论证与模拟证据，并将其应用于学龄儿童校内体力活动与年龄性别调整BMI关联的实证分析。
- 阅读关注点
- 阅读全文时应核对：零值与测量误差的联合建模识别假设、理论结果的适用条件、模拟设计是否覆盖真实数据结构，以及实证中体力活动与BMI关联的因果解释边界。
- 阅读边界
- 仅凭摘要无法判断识别假设是否成立、理论性质的具体形式、模拟与实证结果的方向和显著性；该文为arXiv预印本，未经同行评审验证，不能据此声称已被期刊接收。
原文摘要
Wearable devices are often used in clinical and epidemiological studies to monitor physical activity behavior and its influence on health outcomes. These devices are worn over multiple days to record activity patterns, such as step counts recorded at the minute level, resulting in multi-level, longitudinal, high-dimensional, or functional data. When monitoring patterns of step counts over multiple days, devices may record excess zeros during periods of sedentary behavior or non-wear times. Additionally, it has been demonstrated that the accuracy of wearable devices in monitoring true physical activity patterns depends on the intensity of the activities and wear times. While work on adjusting for biases due to measurement errors in functional data is a growing field, relatively less work has been done to study the occurrence of excess zeros along with measurement errors and their combined influence on estimation and inference in multi-level scalar-on-function regression models. We propose semi-continuous modeling approaches to adjust for biases due to zero inflation and measurement errors in scalar-on-function regression models. We provide theoretical justifications for our proposed methods and, through extensive simulations, we demonstrated their finite sample properties. Finally, the developed methods are applied to a school-based intervention study examining the association between school day physical activity with age- and sex-adjusted body mass index among elementary school-aged children.