# Semiparametric estimation for space-time max-stable processes: F -madogram-based estimation approach

## TL;DR

This paper introduces a semiparametric estimation method based on the F-madogram for modeling extremal dependence in space-time max-stable processes, with applications to rainfall data analysis.

## Contribution

It proposes a novel F-madogram-based inference approach for space-time max-stable processes, addressing complex spatio-temporal dependence modeling.

## Key findings

- Method performs well in simulation studies.
- Successfully applied to real rainfall data.
- Provides reliable extremal dependence estimates.

## Abstract

Max-stable processes have been expanded to quantify extremal dependence in spatio-temporal data. Due to the interaction between space and time, spatio-temporal data are often complex to analyze. So, characterizing these dependencies is one of the crucial challenges in this field of statistics. This paper suggests a semiparametric inference methodology based on the spatio-temporal F-madogram for estimating the parameters of a space-time max-stable process using gridded data. The performance of the method is investigated through various simulation studies. Finally, we apply our inferential procedure to quantify the extremal behavior of radar rainfall data in a region in the State of Florida.