Affiliation:
1. Sorbonne Universités, Université de Technologie de Compiègne, LMAC, Compiègne Cedex, France
Abstract
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.
Subject
Statistics and Probability
Cited by
4 articles.
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