We introduce a Bayesian semiparametric methodology for joint quantile regression with linearity and piecewise linearity constraints. We develop a probability model for all quantile curves in a continuum that define a coherent sampling distribution of the response variable. We provide a detailed illustration of model fitting and inference by analyzing wind speed trends of tropical cyclones in the North Atlantic.</p
A new technique based on Bayesian quantile regression that models the dependence of a quantile of on...
The focus of this work is to develop a Bayesian framework to combine information from multiple parts...
The paper introduces a new class of models, named dynamic quantile linear models, which combines dyn...
Quantile regression has recently received a great deal of attention in both theoretical and empirica...
The classical theory of linear models focuses on the conditional mean function, i.e. the function th...
Quantile regression provides a convenient framework for analyzing the impact of covariates on the co...
We develop a Bayesian method for nonparametric model–based quantile regression. The approach in-volv...
90 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008.Throughout the thesis, we emph...
In the traditional joint models (JM) of a longitudinal and time-to-event data, a linear mixed model ...
Quantile regression, as a supplement to the mean regression, is often used when a comprehensive rel...
Abstract: Quantile regression provides a convenient framework for analyzing the impact of covari-ate...
This paper is a study of the application of Bayesian Exponentially Tilted Empirical Likelihood to in...
Lp–quantiles generalise quantiles and expectiles to account for the whole distribution of the random...
Semiparametric mixed-effects double regression models have been used for analysis of longitudinal da...
This paper illustrates application of Bayesian inference to quantile regression. Bayesian inference ...
A new technique based on Bayesian quantile regression that models the dependence of a quantile of on...
The focus of this work is to develop a Bayesian framework to combine information from multiple parts...
The paper introduces a new class of models, named dynamic quantile linear models, which combines dyn...
Quantile regression has recently received a great deal of attention in both theoretical and empirica...
The classical theory of linear models focuses on the conditional mean function, i.e. the function th...
Quantile regression provides a convenient framework for analyzing the impact of covariates on the co...
We develop a Bayesian method for nonparametric model–based quantile regression. The approach in-volv...
90 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008.Throughout the thesis, we emph...
In the traditional joint models (JM) of a longitudinal and time-to-event data, a linear mixed model ...
Quantile regression, as a supplement to the mean regression, is often used when a comprehensive rel...
Abstract: Quantile regression provides a convenient framework for analyzing the impact of covari-ate...
This paper is a study of the application of Bayesian Exponentially Tilted Empirical Likelihood to in...
Lp–quantiles generalise quantiles and expectiles to account for the whole distribution of the random...
Semiparametric mixed-effects double regression models have been used for analysis of longitudinal da...
This paper illustrates application of Bayesian inference to quantile regression. Bayesian inference ...
A new technique based on Bayesian quantile regression that models the dependence of a quantile of on...
The focus of this work is to develop a Bayesian framework to combine information from multiple parts...
The paper introduces a new class of models, named dynamic quantile linear models, which combines dyn...