Diagnostic Tools for Extreme Value Regression Models

2026-06-01

AI Summary

This study addresses the lack of interpretable, scalable diagnostic tools for extreme value regression models that can identify regions in covariate space where local fit is poor. The authors propose two visualization-based diagnostics—standardized tail plots and normalized residual plots—leveraging the asymptotic distribution of normalized exceedance probabilities to construct sample-size-invariant uncertainty bounds. This enables consistent assessment of both global and local goodness-of-fit. Notably, the approach provides the first framework for local diagnostics in low-dimensional or non-Euclidean covariate domains, supports model comparison across varying sample sizes, and facilitates large-scale model screening. In two real-world applications, the method successfully evaluated thousands of candidate models, yielding actionable modeling recommendations that substantially enhance the reliability and practical utility of extreme value regression models.

Abstract

Visual and quantitative goodness-of-fit diagnostics are an important tool in the practitioner's toolbox. The need for convincing and reliable diagnostics is particularly clear when fitting extreme value regression models, which are used for extrapolation far beyond the observable range of the response variable, and often evaluated at unobserved covariate values. Despite this, few diagnostics have been developed for extreme value regression models, and those available often suffer in terms of interpretability or scalability on low-dimensional or non-Euclidean covariate domains, often encountered in modern applications. Moreover, existing methods tend to offer a global perspective on model fit; that is, they quantify goodness-of-fit across the entire dataset, without offering insight into regions of the covariate space where the model fit may be poor. We propose two novel visual diagnostics for extreme value regression models: the standardised tail plot and the normalised residual plot. By considering the asymptotic distribution of normalised exceedance probabilities, we show that uncertainty bounds for our plots are approximately independent of the sample size used in their construction. This allows us to propose visual diagnostics which can efficiently and consistently compare goodness-of-fit at both a global and regional level, despite varying sample sizes over regions of the covariate domain. Following a discussion of summary statistics for global and regional goodness-of-fit, we provide two applications of extreme value regression models that illustrate how our diagnostics can be used to perform model comparison (across thousands of candidate models) and provide actionable findings that support model design.

Problem

• extreme value regression
• goodness-of-fit diagnostics
• covariate space
• model fit assessment
• non-Euclidean covariates

Innovation

• extreme value regression
• goodness-of-fit diagnostics
• standardised tail plot
• normalised residual plot
• asymptotic distribution