Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Flexible shrinkage in high-dimensional Bayesian spatial autoregressive
  models   This article introduces two absolutely continuous global-local shrinkage
priors to enable stochastic variable selection in the context of
high-dimensional matrix exponential spatial specifications. Existing approaches
as a means to dealing with overparameterization problems in spatial
autoregressive specifications typically rely on computationally demanding
Bayesian model-averaging techniques. The proposed shrinkage priors can be
implemented using Markov chain Monte Carlo methods in a flexible and efficient
way. A simulation study is conducted to evaluate the performance of each of the
shrinkage priors. Results suggest that they perform particularly well in
high-dimensional environments, especially when the number of parameters to
estimate exceeds the number of observations. For an empirical illustration we
use pan-European regional economic growth data.

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The categories of this paper are: gr-qc
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The categories of this paper are: math-ph
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The categories of this paper are: stat
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The categories of this paper are: quant-ph
3
The categories of this paper are: physics
12
The categories of this paper are: nucl-th
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The categories of this paper are: q-fin
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The categories of this paper are: q-alg
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The categories of this paper are: astro-ph
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The categories of this paper are: cond-mat
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The categories of this paper are: patt-sol
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The categories of this paper are: chao-dyn
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The categories of this paper are: hep-lat
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The categories of this paper are: dg-ga
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The categories of this paper are: mtrl-th
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The categories of this paper are: eess