On posterior concentration rates for Bayesian quantile regression based on the misspecified asymmetric Laplace likelihood

Abstract: The asymmetric Laplace density (ALD) is used as a working likelihood for Bayesian quantile regression. Sriram et al.(2013) derived posterior consistency for Bayesian linear quantile regression based on the misspecified ALD. While their paper also argued for $\sqrt{n}-$consistency, Sriram and Ramamoorthi (2017) highlighted that the argument was only valid for a rate less than $\sqrt{n}$. So, the question of $\sqrt{n}-$rate has remained unaddressed, but is necessary to carry out meaningful Bayesian inference based on the ALD. In this paper, we derive results to obtain posterior consistency rates for Bayesian quantile regression based on the misspecified ALD. We derive our results in a slightly general setting where the quantile function can possibly be non-linear. In particular, we give sufficient conditions for $\sqrt{n}-$consistency for the Bayesian linear quantile regression using ALD. We also provide examples of Bayesian non-linear quantile regression.