Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models

  title={Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models},
  author={Michael Pfarrhofer and Philipp Piribauer},
  journal={Spatial Statistics},
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… Expand
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