发表机构
Department of Statistics, University of California, Davis（加州大学戴维斯分校统计系）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出gMiss框架，利用图扫描结合逐元素与距离插补，检测部分观测高维数据中的分布变点，无需稀疏或高斯假设，并在模拟和基因组数据中验证了其有效性。
AI 中文摘要
部分缺失在高维数据中普遍存在，但大多数现有的变点检测程序是为完全观测序列设计的。我们引入了gMiss，一个基于图的框架，用于测试和定位部分观测高维序列的观测数据分布中的变点。该方法将观测值与缺失指示符一起作为推断对象，因此目标备择假设是诱导观测数据定律的变化。它适用于一般的分布变化，既不需要稀疏性也不需要高斯性。当增广观测独立时，完全置换检验在有限样本中控制第一类错误。该程序结合了基于逐元素插补和距离插补的图扫描。这两种扫描捕获互补的图模式。模拟结果表明，gMiss在考虑的MCAR和MAR设计下保持准确的零分布校准，在高斯位置备择下保持竞争力，并在许多非高斯位置和尺度设置中表现出强大的功效和定位性能。我们进一步通过应用于基因组拷贝数数据的实例说明了该方法的实用价值，其中gMiss识别出在原始热图中视觉上合理的额外候选边界。
英文摘要
Partial missingness is common in high-dimensional data, but most existing change-point procedures are developed for fully observed sequences. We introduce gMiss, a graph-based framework for testing and localizing a change in the observed-data distribution of a partially observed high-dimensional sequence. The method treats the observed values together with the missingness indicators as the object of inference, so the target alternative is a change in the induced observed data law. It is designed for general distributional changes and requires neither sparsity nor Gaussianity. When the augmented observations are independent, the full permutation test controls type I error in finite samples. The procedure combines graph scans based on elementwise imputation and distance imputation. The two scans capture complementary graph patterns. Simulation results indicate that gMiss maintains accurate null calibration across the MCAR and MAR designs considered, remains competitive under Gaussian location alternatives, and exhibits strong power and localization performance in many non-Gaussian location and scale settings. We further illustrate the practical utility of the method through an application to genomic copy-number data, where gMiss identifies additional candidate boundaries that are visually plausible in the raw heatmap.
