Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: The EAS approach for graphical selection consistency in vector
  autoregression models   As evidenced by various recent and significant papers within the frequentist
literature, along with numerous applications in macroeconomics, genomics, and
neuroscience, there continues to be substantial interest to understand the
theoretical estimation properties of high-dimensional vector autoregression
(VAR) models. To date, however, while Bayesian VAR (BVAR) models have been
developed and studied empirically (primarily in the econometrics literature)
there exist very few theoretical investigations of the repeated sampling
properties for BVAR models in the literature. In this direction, we construct
methodology via the $\varepsilon$-$admissible$ subsets (EAS) approach for
posterior-like inference based on a generalized fiducial distribution of
relative model probabilities over all sets of active/inactive components
(graphs) of the VAR transition matrix. We provide a mathematical proof of
$pairwise$ and $strong$ graphical selection consistency for the EAS approach
for stable VAR(1) models which is robust to model misspecification, and
demonstrate numerically that it is an effective strategy in high-dimensional
settings.

28
The categories of this paper are: alg-geom
36
The categories of this paper are: cmp-lg
20
The categories of this paper are: acc-phys
29
The categories of this paper are: funct-an
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc
19
The categories of this paper are: eess
32
The categories of this paper are: atom-ph
13
The categories of this paper are: quant-ph
31
The categories of this paper are: ao-sci
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
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The categories of this paper are: dg-ga
8
The categories of this paper are: hep-ex
33
The categories of this paper are: plasm-ph
3
The categories of this paper are: physics