Amina El Bernoussi | Searcharxiv

arXiv2025

A simulation study comparing statistical approaches for estimating extreme quantile regression with an application to forecasting of fire risk

This simulation study compares statistical approaches for estimating extreme quantile regression, with a specific application to fire risk forecasting. A simulation-based framework is designed to evaluate the effectiveness of different methods in capturing extreme dependence structures and accurately predicting extreme quantiles. These approaches are applied to fire occurrence data from the Fez-Meknes region, where a positive relationship is observed between increasing maximum temperatures and fire frequency. The study highlights the comparative performance of each technique and advocates for a hybrid strategy that combines their complementary strengths to enhance both the accuracy and interpretability of forecasts for extreme events.

stat.ME↗

arXiv2025

A New Regression Model for Analyzing Non-Stationary Extremes in Response and Covariate Variables with an Application in Meteorology

The paper introduces a new regression model designed for situations where both the response and covariates are non-stationary extremes. This method is specifically designed for situations where both the response variable and covariates are represented as block maxima, as the limiting distribution of suitably standardized componentwise maxima follows an extreme value copula. The framework focuses on the regression manifold, which consists of a collection of regression lines aligned with the asymptotic result. A Logistic-normal prior is applied to the space of spectral densities to gain insights into the model based on the data, resulting in an induced prior on the regression manifolds. Numerical studies demonstrate the effectiveness of the proposed method, and an analysis of real meteorological data provides intriguing insights into the relationships between extreme losses in precipitation and temperature.

stat.ME↗