Simultaneous Linear Quantile Regression: A Semiparametric Bayesian Approach

Simultaneous Linear Quantile Regression: A Semiparametric Bayesian Approach

We introduce a semi-parametric Bayesian framework for a simultaneous analysis of linear quantile regression models. A simultaneous analysis is essential to attain the true potential of the quantile regression framework, but is computationally challenging due to the associated monotonicity constraint on the quantile curves. For a univariate covariate, we present a simpler equivalent characteriza...

Bayesian semiparametric additive quantile regression

Quantile regression provides a convenient framework for analyzing the impact of covariates on the complete conditional distribution of a response variable instead of only the mean. While frequentist treatments of quantile regression are typically completely nonparametric, a Bayesian formulation relies on assuming the asymmetric Laplace distribution as auxiliary error distribution that yields po...

www.econstor.eu Bayesian semiparametric additive quantile regression

Efficient Semiparametric Estimation of a Partially Linear Quantile Regression Model

This paper is concerned with estimating a conditional quantile function that is assumed to be partially linear+ The paper develops a simple estimator of the parametric component of the conditional quantile+ The semiparametric efficiency bound for the parametric component is derived, and two types of efficient estimators are considered+ Asymptotic properties of the proposed estimators are establ...

Semiparametric Approach to a Random Effects Quantile Regression Model.

We consider a random effects quantile regression analysis of clustered data and propose a semiparametric approach using empirical likelihood. The random regression coefficients are assumed independent with a common mean, following parametrically specified distributions. The common mean corresponds to the population-average effects of explanatory variables on the conditional quantile of interest...