Empirical Likelihood-Based Inference for Functional Means with Application to Wearable Device Data
针对可穿戴设备数据中的占用时间曲线，提出一种非参数推断框架，允许函数协方差存在不连续性并适应轨迹离散化，通过似然比方法构建置信带和比较函数均值，模拟显示优于现有方法。
Abstract 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.