Graphical Assistant Grouped Network Autoregression Model: A Bayesian Nonparametric Recourse
Graphical Assistant Grouped Network Autoregression Model: A Bayesian Nonparametric Recourse
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DOI：
10.1080/07350015.2022.2143784
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发表时间：
2021-10
影响因子：
3
通讯作者：
Yi Ren;Xuening Zhu;Xiaoling Lu;Guanyu Hu
中科院分区：
文献类型：
--
作者：
Yi Ren;Xuening Zhu;Xiaoling Lu;Guanyu Hu
摘要 向量自回归模型在经典时间序列数据分析中普遍存在。随着社交网站的快速发展，潜在图上的时间序列数据变得越来越流行。在本文中，我们开发了一种新颖的贝叶斯分组网络自回归模型，该模型可以同时估计组信息（组数和组配置）和分组参数。具体来说，在网络自回归模型的框架下纳入图形辅助的中餐馆流程，以提高统计推理性能。使用高效的马尔可夫链蒙特卡罗采样算法对后验分布进行采样。进行了广泛的研究来评估我们提出的方法的有限样本性能。此外，我们分析了两个真实数据集，以说明我们方法的有效性。
Abstract Vector autoregression model is ubiquitous in classical time series data analysis. With the rapid advance of social network sites, time series data over latent graph is becoming increasingly popular. In this article, we develop a novel Bayesian grouped network autoregression model, which can simultaneously estimate group information (number of groups and group configurations) and group-wise parameters. Specifically, a graphically assisted Chinese restaurant process is incorporated under the framework of the network autoregression model to improve the statistical inference performance. An efficient Markov chain Monte Carlo sampling algorithm is used to sample from the posterior distribution. Extensive studies are conducted to evaluate the finite sample performance of our proposed methodology. Additionally, we analyze two real datasets as illustrations of the effectiveness of our approach.