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arXiv · 1605.03471

Nonparametric hierarchical Bayesian quantiles

Luke Bornn·Neil Shephard·Reza Solgi
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Abstract

Here we develop a method for performing nonparametric Bayesian inference on quantiles. Relying on geometric measure theory and employing a Hausdorff base measure, we are able to specify meaningful priors for the quantile while treating the distribution of the data otherwise nonparametrically. We further extend the method to a hierarchical model for quantiles of subpopulations, linking subgroups together solely through their quantiles. Our approach is computationally straightforward, allowing for censored and noisy data. We demonstrate the proposed methodology on simulated data and an applied problem from sports statistics, where it is observed to stabilize and improve inference and prediction.

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Luke Bornn, Neil Shephard, Reza Solgi. 2016-05-11. Nonparametric hierarchical Bayesian quantiles. https://arxiv.org/abs/1605.03471

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