论文标题
功能回归，具有深入测量的纵向结果：通过数据分配的新镜头
Functional Regression with Intensively Measured Longitudinal Outcomes: A New Lens through Data Partitioning
论文作者
论文摘要
大量计算成本负担重大的计算成本负担，对可穿戴设备的现代纵向数据的估计和推断，这些纵向数据由高频时间点的生物信号组成。我们提出了一个分布式估计和推理程序，该过程有效地估算了具有纵向结果的功能和标量参数。该过程通过可扩展的划分和争议算法来克服计算困难，该算法将结果分为较小的集合。我们通过使用较小子集中的二次推理功能分析数据来避免传统的基础选择问题，从而使基础函数具有较低的维度。为了解决从依赖子集组合估计值的挑战，我们提出了一个统计上有效的一步估计器，该估计量是从约束的矩矩目标函数和平滑惩罚的受约束的广义方法得出的。我们从理论上和数字上表明，所提出的估计器在统计上与非分布的替代方法一样有效，并且在计算上具有更有效的计算。通过分析国家健康和营养检查调查的加速度计数据，我们证明了我们方法的实用性。
Estimation and inference with modern longitudinal data from wearable devices, which consist of biological signals at high-frequency time points, is burdened by massive computational costs. We propose a distributed estimation and inference procedure that efficiently estimates both functional and scalar parameters with intensively measured longitudinal outcomes. The procedure overcomes computational difficulties through a scalable divide-and-conquer algorithm that partitions the outcomes into smaller sets. We circumvent traditional basis selection problems by analyzing data using quadratic inference functions in smaller subsets such that the basis functions have a low dimension. To address the challenges of combining estimates from dependent subsets, we propose a statistically efficient one-step estimator derived from a constrained generalized method of moments objective function with a smoothing penalty. We show theoretically and numerically that the proposed estimator is as statistically efficient as non-distributed alternative approaches and more efficient computationally. We demonstrate the practicality of our approach with the analysis of accelerometer data from the National Health and Nutrition Examination Survey.
