Bayesian analysis of a Tobit quantile regression model

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Gibbs sampling methods for Bayesian quantile regression (2011, methodology): “Thus, our approach offers a simple sampling method for Tobit quantile regression models compared with that of Yu and Stander [18], which relies on Metropolis–Hastings algorithms and requires to control tuning parameters for each value of p.”
A Bayesian Nonparametric Approach to Inference for Quantile Regression (2010, methodology): “advantage over parametric formulations (as in, e.g., Yu and Stander, 2007 ) that postulate a”
A partially collapsed Gibbs sampler for Bayesian quantile regression (2009, methodology): “…RJMCMC (Reversible Jump Markov Chain Monte Carlo) methods via an asymmetric Laplace distribution for the likelihood function (Yu and Moyeed, 2001; Yu and Stander, 2007; Chen and Yu, 2008; Tsionas, 2003), Dirichlet process mixing based nonparametric median zero distribution for the regression…”
Posterior Inference in Bayesian Quantile Regression with Asymmetric Laplace Likelihood (2016, methodology): “In fact, when D 1 as used in Yu & Stander (2007), the 95% intervals on the quantile coefficients are remarkably narrow, but they cannot be taken seriously.”