研究人员开发了一种新的统计框架，用于推断高维时间序列数据中的条件依赖结构。该方法解决了离散傅里叶变换引入偏差以及谱精度矩阵的复数值性质带来的挑战。所提出的方法利用相邻离散傅里叶变换的完整似然来构建一个无偏的图套索估计器，从而实现更准确的推断和提高检测能力。 AI
排序理由 该集群包含一篇详细介绍新统计方法的学术论文。[lever_c_demoted from research: ic=2 ai=0.4]
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arXiv:2606.07986v1 Announce Type: cross Abstract: Gaussian graphical models in the spectral domain offer a principled approach for recovering conditional dependence structures in stationary high-dimensional time series. Inference on the spectral precision matrix at a fixed freque…
Gaussian graphical models in the spectral domain offer a principled approach for recovering conditional dependence structures in stationary high-dimensional time series. Inference on the spectral precision matrix at a fixed frequency enables tests of frequency-specific conditiona…