标题： 部分观测功能数据中的均值变化检测 显示英文标题

标题： Detection of mean changes in partially observed functional data

摘要： 我们提出了一种用于检测功能观测序列均值变化的检验方法，这些观测序列仅在域的部分子集上被部分观测，且对于补集没有任何信息。 该框架能够容纳重要的场景，包括突然变化和渐变变化。 通过置换检验来评估检验统计量的显著性。 除了经典的固定置换样本数量的置换方法外，我们还讨论了一种依赖于随机（数据驱动）置换样本数量的控制抽样风险的变体。 所提出的方法在小样本情况下的性能通过蒙特卡洛模拟研究和对实际数据的应用进行了说明。 显示英文摘要

摘要： We propose a test for a change in the mean for a sequence of functional observations that are only partially observed on subsets of the domain, with no information available on the complement. The framework accommodates important scenarios, including both abrupt and gradual changes. The significance of the test statistic is assessed via a permutation test. In addition to the classical permutation approach with a fixed number of permutation samples, we also discuss a variant with controlled resampling risk that relies on a random (data-driven) number of permutation samples. The small sample performance of the proposed methodology is illustrated in a Monte Carlo simulation study and an application to real data.