Bayesian Lasso Quantile Regression. Feb 08 2019 Many researchers used quantile regression approach to model this case but this method has limitation. Bayesian Lasso Binary Quantile Regression 5 binary quantile regression model are ideally suited to tackle the problem of predictor dimension reduction in binary datasets.
May 18 2012 In this paper we propose adaptive Lasso quantile regression BALQR from a Bayesian perspective. Feb 08 2019 Many researchers used quantile regression approach to model this case but this method has limitation. The asymmetric Laplace likelihood has a special place.
The limitation of this approach is need moderate to big sample size.
In particular we propose a new Bayesian Lasso method that employs a skewed Laplace distribution for the errors and a scaled mixture of uniform distribution for the regression parameters together with Bayesian MCMC estimation. The objective of this paper is to illustrate Brq a new software package in R. In this paper we develop a fully Bayesian adaptive Lasso approach for quantile regression models with nonignorably missing response data where the nonignorable missingness mechanism is specified by a logistic regression model. The asymmetric Laplace error distribution is written as a scale mixture of normals as in Reed and Yu 2009.
