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Scalable Bayesian nonparametric regression via a Plackett-Luce model for conditional ranks.

ABSTRACT: We present a novel Bayesian nonparametric regression model for covariates X and continuous response variable Y ? ?. 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.

SUBMITTER: Gray-Davies T

PROVIDER: S-EPMC5881922 | biostudies-literature | 2016 Jul

REPOSITORIES: biostudies-literature

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Scalable Bayesian nonparametric regression via a Plackett-Luce model for conditional ranks.

Gray-Davies Tristan T Holmes Chris C CC Caron François F

Electronic journal of statistics 20160701 2

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. T ...[more]

PMID: 29623150

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