Graphical Assistant Grouped Network Autoregression Model: A Bayesian Nonparametric Recourse
提出一种贝叶斯分组网络自回归模型，能同时估计分组信息和组内参数，适用于带潜在图结构的时间序列数据，通过图形辅助的中国餐馆过程提升推断性能。
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.