A Bayesian Approach to Multiple-Output Quantile Regression Analysis under Informative Sampling

Abstract This article presents a Bayesian multiple-output quantile regression for complex survey data under informative sampling. Our approach relies on the asymmetric Laplace distributional assumption. From the location-scale mixture representation of this distribution, we introduce an Expectation–Maximization algorithm that provides a less computationally intensive alternative to the commonly used Markov Chain Monte Carlo algorithm for posterior inference. Our developments are mainly motivated by the joint analysis of growth indexes for Brazilian children under five.