Bayesian Quantile Regression Methods. Sep 04 2017 In this work we propose a Bayesian quantile regression method to response variables with mixed discrete-continuous distribution with a point mass at zero where these observations are believed to be left censored or true zeros. Apr 29 2016 This paper investigates regularization regression from Bayesian perspective.
Apr 29 2016 This paper investigates regularization regression from Bayesian perspective. It is shown that irrespective of the original distribution of the data the use of the asymmetric Laplace distribution is a very natural and effective way for modelling Bayesian quantile regression. In this paper we explain how this method may be applied to quantile regression both when regressor variables are exogenous and when they are endogenous but instrumental variables are available.
Mar 01 2010 Bayesian quantile regression methods INTRODUCTION Recent work by Schennach 2005 has opened the way to a new Bayesian treatment of quantile regression.
Apr 29 2016 This paper investigates regularization regression from Bayesian perspective. This paper considers Bayesian quantile regression models using the asymmetric Laplace distribution and proposes MCMC methods that are not only computationally eļ¬cient but also easy to implement. We also examine Markov chain Monte Carlo simulations with a proposal density formed from an overdispersed version of the limiting normal density. This paper considers quantile regression models using an asymmetric Laplace distribution from a Bayesian point of view.
