提出新方法估计非因果图模型，能捕捉变量间平滑关系。
Identification of Non-causal Graphical Models
- 通过最小化与白噪声的运输距离求解协方差扩展问题
- 解为双侧自回归非因果图模型，可描述变量间平滑依赖
- 推广至图自回归移动平均模型，适合处理复杂依赖结构
本文研究非因果图模型的估计问题，其中边表示变量间的平滑关系。提出一种新的协方差扩展问题，并证明使与白噪声过程运输距离最小的解为双侧自回归非因果图模型。进一步将该范式推广到一类图自回归移动平均模型。最后通过数值实验验证了所提方法的性能。
原文摘要 · Abstract (English)
The paper considers the problem to estimate non-causal graphical models whose edges encode smoothing relations among the variables. We propose a new covariance extension problem and show that the solution minimizing the transportation distance with respect to white noise process is a double-sided autoregressive non-causal graphical model. Then, we generalize the paradigm to a class of graphical autoregressive moving-average models. Finally, we test the performance of the proposed method through some numerical experiments.
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