Graphical Assistant Grouped Network Autoregression Model: A Bayesian Nonparametric Recourse

Vector autoregression model is ubiquitous in classical time series data analysis. With the rapid advance of social network sites, over latent graph becoming increasingly popular. In this paper, we develop a novel Bayesian grouped to simultaneously estimate group information (number groups and configurations) group-wise parameters. Specifically, graphically assisted Chinese restaurant process incorporated under framework improve statistical inference performance. An efficient Markov chain Monte Carlo sampling algorithm used sample from posterior distribution. Extensive studies are conducted evaluate finite performance our proposed methodology. Additionally, analyze two real datasets as illustrations effectiveness approach.