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arXiv · 2306.02126

A Process of Dependent Quantile Pyramids

Hyoin An·Steven N. MacEachern
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Abstract

Despite the practicality of quantile regression (QR), simultaneous estimation of multiple QR curves continues to be challenging. We address this problem by proposing a Bayesian nonparametric framework that generalizes the quantile pyramid by replacing each scalar variate in the quantile pyramid with a stochastic process on a covariate space. We propose a novel approach to show the existence of a quantile pyramid for all quantiles. The process of dependent quantile pyramids allows for non-linear QR and automatically ensures non-crossing of QR curves on the covariate space. Simulation studies document the performance and robustness of our approach. An application to cyclone intensity data is presented.

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Hyoin An, Steven N. MacEachern. 2023-06-03. A Process of Dependent Quantile Pyramids. https://arxiv.org/abs/2306.02126

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