Bayesian Quantile Regression

Authored by: Huixia Judy Wang , Yunwen Yang

Handbook of Quantile Regression

Print publication date:  October  2017
Online publication date:  October  2017

Print ISBN: 9781498725286
eBook ISBN: 9781315120256
Adobe ISBN:

10.1201/9781315120256-4

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Abstract

Bayesian approaches provide convenient alternative inference tools for quantile regression. Even though conventional quantile regression does not require any parametric distributional assumptions, a working likelihood is needed to carry out Bayesian analysis. In this chapter, we provide a review of Bayesian quantile regression methods based on different types of working likelihoods.

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