A Bayesian Variable Selection Method for Spatial Autoregressive Quantile Models

In this paper, a Bayesian variable selection method for spatial autoregressive (SAR) quantile models is proposed on the basis of spike and slab prior regression parameters. The SAR models, which are more generalized than specified by adopting asymmetric Laplace distribution error term in classical models. approach could perform simultaneously robust parametric estimation context statistical inferences implemented detailed Markov chain Monte Carlo (MCMC) procedure that combines Gibbs samplers with probability integral transformation (PIT) algorithm. end, empirical numerical examples including several simulation studies Boston housing price data analysis employed to demonstrate newly developed methodologies.