Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: 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, time series data over
latent graph is becoming increasingly popular. In this paper, we develop a
novel Bayesian grouped network autoregression model to simultaneously estimate
group information (number of groups and group configurations) and group-wise
parameters. Specifically, a graphically assisted Chinese restaurant process is
incorporated under 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.

23
The categories of this paper are: solv-int
0
The categories of this paper are: hep-ph
5
The categories of this paper are: gr-qc
30
The categories of this paper are: q-alg
21
The categories of this paper are: adap-org
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
18
The categories of this paper are: econ
4
The categories of this paper are: cond-mat
33
The categories of this paper are: plasm-ph
19
The categories of this paper are: eess
17
The categories of this paper are: q-fin
9
The categories of this paper are: nlin
1
The categories of this paper are: cs
34
The categories of this paper are: chem-ph
2
The categories of this paper are: math