A Bayesian Approach to Multiple-Output Quantile Regression

August, 2019
IDA document: P-10832
FFRDC: Systems and Analyses Center
Type: Documents
Division: Strategy, Forces and Resources Division
Authors:

Authors

Michael Guggisberg See more authors

Download Publication

document cover, A Bayesian Approach to Multiple-Output Quantile Regression

This paper presents a Bayesian approach to multiple-output quantile regression. The unconditional model is proven to be consistent and asymptotically correct frequentist confidence intervals can be obtained. The prior for the unconditional model can be elicited as the ex-ante knowledge of the distance of the τ-Tukey depth contour to the Tukey median, the first prior of its kind. A proposal for conditional regression is also presented.