Fully Bayesian Estimation of Simultaneous Regression Quantiles under Asymmetric Laplace Distribution Specification

Abstract EN

In this paper, we are interested in estimating several quantiles simultaneously in a regression context via the Bayesian approach.

Assuming that the error term has an asymmetric Laplace distribution and using the relation between two distinct quantiles of this distribution, we propose a simple fully Bayesian method that satisfies the noncrossing property of quantiles.

For implementation, we use Metropolis-Hastings within Gibbs algorithm to sample unknown parameters from their full conditional distribution.

The performance and the competitiveness of the underlying method with other alternatives are shown in simulated examples.