Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models

@article{Pfarrhofer2019FlexibleSI,
  title={Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models},
  author={Michael Pfarrhofer and Philipp Piribauer},
  journal={Spatial Statistics},
  year={2019}
}
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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References

SHOWING 1-10 OF 45 REFERENCES
Bayesian Variable Selection in Spatial Autoregressive Models
Abstract This paper compares the performance of Bayesian variable selection approaches for spatial autoregressive models. It presents two alternative approaches that can be implemented using GibbsExpand
Adaptive Shrinkage in Bayesian Vector Autoregressive Models
ABSTRACT Vector autoregressive (VAR) models are frequently used for forecasting and impulse response analysis. For both applications, shrinkage priors can help improving inference. In this article,Expand
Sparse Bayesian time-varying covariance estimation in many dimensions
  • G. Kastner
  • Mathematics, Economics
  • Journal of Econometrics
  • 2019
Dynamic covariance estimation for multivariate time series suffers from the curse of dimensionality. This renders parsimonious estimation methods essential for conducting reliable statisticalExpand
Bayesian Estimation of Spatial Autoregressive Models
Spatial econometrics has relied extensively on spatial autoregressive models. Anselin (1988) developed a taxonomy of these models using a regression model framework and maximum likelihood estimationExpand
Achieving shrinkage in a time-varying parameter model framework
TLDR
An efficient Markov chain Monte Carlo scheme is developed, exploiting boosting based on the ancillarity-sufficiency interweaving strategy to automatically reduce time-varying parameters to static ones, if the model is overfitting. Expand
Dirichlet–Laplace Priors for Optimal Shrinkage
TLDR
This article proposes a new class of Dirichlet–Laplace priors, which possess optimal posterior concentration and lead to efficient posterior computation. Expand
Is a matrix exponential specification suitable for the modeling of spatial correlation structures?
TLDR
Both the applications and the simulations suggest that the spatial splines are a flexible and efficient way to account for spatial heterogeneities governed by unknown mechanisms. Expand
Shrink Globally, Act Locally: Sparse Bayesian Regularization and Prediction
We study the classic problem of choosing a prior distribution for a location parameter β = (β1, . . . , βp) as p grows large. First, we study the standard “global-local shrinkage” approach, based onExpand
Inference with normal-gamma prior distributions in regression problems
This paper considers the efiects of placing an absolutely continuous prior distribution on the regression coe-cients of a linear model. We show that the posterior expectation is a matrix-shrunkenExpand
A matrix exponential spatial specification
We introduce the matrix exponential as a way of modelling spatially dependent data. The matrix exponential spatial specification (MESS) simplifies the log-likelihood allowing a closed form solutionExpand
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