May 25, 2025 · This paper introduces a Bayesian quantile regression model with linear inequality constraints. To rigorously enforce these inequality constraints within the Bayesian framework, we …

Bayesian quantile regression using the asymmetric Laplace distribution, both continu-ous as well as binary dependent variables are supported.

Oct 15, 2001 · In this paper, we adopt a Bayesian approach to quantile regression which is quite different from what has been done previously in this context.

Here we demonstrate a univariate Bayesian quantile regression. For other Bayesian quantile models, including random effects and multiple quantiles, see Bayesian asymmetric Laplace model.

This paper introduces Bayesian smoothed quantile regression (BSQR) to fill this gap, proposing a framework that resolves both limitations through a principled reformulation of the likelihood.

May 25, 2025 · This paper introduces a Bayesian quantile regression model with linear inequality constraints. To rigorously enforce these inequality constraints within the Bayesian framework, we …

Bayesian quantile regression extends the classical quantile regression framework by embedding it within a probabilistic paradigm, allowing direct inference on conditional quantiles through...

Oct 10, 2025 · This paper introduces a Bayesian quantile regression-based semiparametric mixed-effects double regression model for examining longitudinal data with non-ignorable missing responses.

The function bayesQR can estimate four types of models, depending on whether the dependent variable is continuous or binary and whether adaptive lasso variable selection is used.

We propose Bayesian smoothed quantile regression (BSQR), a principled framework built on a kernel- smoothed, fully differentiable likelihood. Methodologically, the symmetrizing property of our objective …