# An improved method for the estimation of the Gumbel distribution parameters

## TL;DR

This paper introduces a new estimation method for Gumbel distribution parameters that leverages all available data and parameter relationships to improve accuracy, especially with limited data samples.

## Contribution

The paper proposes an innovative approach using informative priors based on parameter relationships to enhance Gumbel parameter estimation from small datasets.

## Key findings

- Improved accuracy over standard methods with limited data
- Reduced credible interval widths in estimations
- Enhanced parameter location accuracy

## Abstract

Usual estimation methods for the parameters of extreme values distribution employ only a few values, wasting a lot of information. More precisely, in the case of the Gumbel distribution, only the block maxima values are used. In this work, we propose a method to seize all the available information in order to increase the accuracy of the estimations. This intent can be achieved by taking advantage of the existing relationship between the parameters of the baseline distribution, which generates data from the full sample space, and the ones for the limit Gumbel distribution. In this way, an informative prior distribution can be obtained. Different statistical tests are used to compare the behaviour of our method with the standard one, showing that the proposed method performs well when dealing with very shortened available data. The empirical effectiveness of the approach is demonstrated through a simulation study and a case study. Reduction in the credible interval width and enhancement in parameter location show that the results with improved prior adapt to very shortened data better than standard method does.