© 2016, Institute of Mathematical Statistics. All rights reserved. We present a novel Bayesian nonparametric regression model for covariates X and continuous response variable Y ∈ R. The model is parametrized in terms of marginal distributions for Y and X and a regression function which tunes the stochastic ordering of the conditional distributions F(y|x). By adopting an approximate composite likelihood approach, we show that the resulting posterior inference can be decoupled for the separate components of the model. This procedure can scale to very large datasets and allows for the use of standard, existing, software from Bayesian nonparametric density estimation and Plackett-Luce ranking estimation to be applied. As an illustration, we show an application of our approach to a US Census dataset, with over 1,300,000 data points and more than 100 covariates.

Original publication

DOI

10.1214/15-EJS1032

Type

Journal article

Journal

Electronic Journal of Statistics

Publication Date

01/01/2016

Volume

10

Pages

1807 - 1828