Add to ORKG90 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 2008.Throughout the thesis, we emphasize that quantile regression provides a nonparametric method to construct the probabilistic model, the likelihood, so it provide a simple but powerful strategy for semiparametric Bayesian methods.U of I OnlyRestricted to the U of I community idenfinitely during batch ingest of legacy ETD
Traditional frequentist quantile regression makes few assumptions on the form of the error distribut...
Bayesian inference can be extended to probability distributions defined in terms of their inverse di...
A guide to the implementation and interpretation of Quantile Regression models. This book explores t...
Quantile regression has recently received a great deal of attention in both theoretical and empirica...
This paper is a study of the application of Bayesian Exponentially Tilted Empirical Likelihood to in...
The classical theory of linear models focuses on the conditional mean function, i.e. the function th...
Quantile regression offers a more complete statistical model than mean regression and now has widesp...
We develop a Bayesian method for nonparametric model–based quantile regression. The approach in-volv...
We introduce a Bayesian semiparametric methodology for joint quantile regression with linearity and ...
Dissertation submitted in fulfillment of the requirements for the degree of doctor in applied econom...
Quantile regression, as introduced by Koenker and Bassett (1978), may be viewed as an extension of c...
A comprehensive treatment of the subject, encompassing models that are linear and nonlinear, paramet...
Quantile regression, as a supplement to the mean regression, is often used when a comprehensive rel...
Recent work by Schennach(2005) has opened the way to a Bayesian treat-ment of quantile regression. H...
The work of three leading "gures in the early history of econometrics is used to motivate some ...
Traditional frequentist quantile regression makes few assumptions on the form of the error distribut...
Bayesian inference can be extended to probability distributions defined in terms of their inverse di...
A guide to the implementation and interpretation of Quantile Regression models. This book explores t...
Quantile regression has recently received a great deal of attention in both theoretical and empirica...
This paper is a study of the application of Bayesian Exponentially Tilted Empirical Likelihood to in...
The classical theory of linear models focuses on the conditional mean function, i.e. the function th...
Quantile regression offers a more complete statistical model than mean regression and now has widesp...
We develop a Bayesian method for nonparametric model–based quantile regression. The approach in-volv...
We introduce a Bayesian semiparametric methodology for joint quantile regression with linearity and ...
Dissertation submitted in fulfillment of the requirements for the degree of doctor in applied econom...
Quantile regression, as introduced by Koenker and Bassett (1978), may be viewed as an extension of c...
A comprehensive treatment of the subject, encompassing models that are linear and nonlinear, paramet...
Quantile regression, as a supplement to the mean regression, is often used when a comprehensive rel...
Recent work by Schennach(2005) has opened the way to a Bayesian treat-ment of quantile regression. H...
The work of three leading "gures in the early history of econometrics is used to motivate some ...
Traditional frequentist quantile regression makes few assumptions on the form of the error distribut...
Bayesian inference can be extended to probability distributions defined in terms of their inverse di...
A guide to the implementation and interpretation of Quantile Regression models. This book explores t...