Pfarrhofer, Michael, Piribauer, Philipp. 2018. Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models.

RIS

@MISC{Pfarrhofer2018,
title = {Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models},
author = {Michael Pfarrhofer and Philipp Piribauer},
year = {2018},
url = {https://arxiv.org/abs/1805.10822},
language = {EN},
abstract = {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.},
}