2018/04/10 by Tullia Padellini, Padellini, Tullia, Rue, Haavard
Computer Science · #FOS: Computer and information sciences #Methodology (stat.ME) #Neural Networks and Applications
paper · pdf · doi:10.48550/arxiv.1804.03714
openalex publication_date 2018/04/10 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Quantile regression relates the quantile of the response to a linear predictor. For a discrete response distributions, like the Poission, Binomial and the negative Binomial, this approach is not feasible as the quantile function is not bijective. We argue to use a continuous model-aware interpolation of the quantile function, allowing for proper quantile inference while retaining model interpretation. This approach allows for proper uncertainty quantification and mitigates the issue of quantile crossing. Our reanalysis of hospitalisation data considered in Congdon (2017) shows the advantages of our proposal as well as introducing a novel method to exploit quantile regression in the context of disease mapping.