研究人员开发了一种新的统计模型，用于分析来自相关来源的高维数据，解决了样本量小时的局限性。该模型明确捕获了数据集之间协方差结构的偏共享，通过利用共性并考虑独特性来改进估计。所提出的方法包括一个完整的估计程序，其渐近保证源自随机矩阵理论，并已应用于 COVID-19 大流行早期阶段的金融投资组合构建和脑肿瘤基因表达分析。 AI
影响 引入了一种新颖的统计框架，用于改进复杂相关数据集的分析，可能影响依赖于高维数据解释的领域。
排序理由 该集群包含两篇相同的 arXiv 预印本，详细介绍了一种新的统计方法。 [lever_c_demoted from research: ic=2 ai=0.4]
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arXiv:2607.08123v1 Announce Type: cross Abstract: Statistical analysis of high-dimensional data is often hampered by limited sample sizes, yet auxiliary datasets from related sources are often readily available. When two such datasets share part of their covariance structure, but…
Statistical analysis of high-dimensional data is often hampered by limited sample sizes, yet auxiliary datasets from related sources are often readily available. When two such datasets share part of their covariance structure, but not all of it, exploiting the shared part can sub…