<b>Is the Conditional Density Network more suitable than the Maximum likelihood for fitting the Generalized Extreme Value Distribution?
The Generalized Extreme value Distribution (GEV) has been widely used to assess the probability of extreme weather events and the parameter estimation method is a key factor for improving its quantile estimates. On such background, this study aimed to indicate under which conditions (sample size and...
Main Authors: | Monica Cristina Meschiatti, Gabriel Constantino Blain |
---|---|
Format: | Article |
Language: | English |
Published: |
Universidade Estadual de Maringá
2015-10-01
|
Series: | Acta Scientiarum: Technology |
Subjects: | |
Online Access: | http://186.233.154.254/ojs/index.php/ActaSciTechnol/article/view/27660 |
Similar Items
-
Urbanization in megacities increases the frequency of extreme precipitation events far more than their intensity
by: Louis Marelle, et al.
Published: (2020-01-01) -
Characterization of long period return values of extreme daily temperature and precipitation in the CMIP6 models: Part 1, model evaluation
by: Michael Wehner, et al.
Published: (2020-12-01) -
Developing Low‐Likelihood Climate Storylines for Extreme Precipitation Over Central Europe
by: C. Gessner, et al.
Published: (2023-09-01) -
Statistical Tests for Extreme Precipitation Volumes
by: Victor Korolev, et al.
Published: (2019-07-01) -
Direct Sampling for Spatially Variable Extreme Event Generation in Resampling‐Based Stochastic Weather Generators
by: Jorge Guevara, et al.
Published: (2023-11-01)