Bayesian Quantile Estimation and Regression with Martingale Posteriors

TL;DR

This paper introduces a novel Bayesian nonparametric method for quantile estimation and regression using martingale posteriors, enabling likelihood-free inference and efficient computation, with theoretical guarantees and practical demonstrations.

Contribution

The paper proposes the quantile martingale posterior (QMP), a new Bayesian approach that simplifies quantile inference without strict likelihood-prior assumptions, and offers computational and theoretical advantages.

Findings

QMP enables likelihood-free Bayesian quantile inference.

The method is MCMC-free and parallelizable, with acceleration via Gaussian process approximation.

The approach naturally addresses monotonicity issues in quantile estimation.

Abstract

Quantile estimation and regression within the Bayesian framework is challenging as the choice of likelihood and prior is not obvious. In this paper, we introduce a novel Bayesian nonparametric method for quantile estimation and regression based on the recently introduced martingale posterior (MP) framework. The core idea of the MP is that posterior sampling is equivalent to predictive imputation, which allows us to break free of the stringent likelihood-prior specification. We demonstrate that a recursive estimate of a smooth quantile function, subject to a martingale condition, is entirely sufficient for full nonparametric Bayesian inference. We term the resulting posterior distribution as the quantile martingale posterior (QMP), which arises from an implicit generative predictive distribution. Associated with the QMP is an expedient, MCMC-free and parallelizable posterior computation scheme, which can be further accelerated with an asymptotic approximation based on a Gaussian process. Furthermore, the well-known issue of monotonicity in quantile estimation is naturally alleviated through increasing rearrangement due to the connections to the Bayesian bootstrap. Finally, the QMP has a particularly tractable form that allows for comprehensive theoretical study, which forms a main focus of the work. We demonstrate the ease of posterior computation in simulations and real data experiments.