# Fast parameter estimation of Generalized Extreme Value distribution using Neural Networks

arXiv: https://arxiv.org/abs/2305.04341 · HTML: https://mlsanity.com/paper/2305.04341/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 65 references that are in the map.
- First posted on arXiv 7 May 2023.

## Builds on (most cited first, 12 of 12)

- [Learning Summary Statistic for Approximate Bayesian Computation via Deep Neural Network](https://mlsanity.com/paper/1510.02175/) (2015)
- [Likelihood-Free Parameter Estimation with Neural Bayes Estimators](https://mlsanity.com/paper/2208.12942/) (2022)
- [Neural Networks for Parameter Estimation in Intractable Models](https://mlsanity.com/paper/2107.14346/) (2021)
- [Fast covariance parameter estimation of spatial Gaussian process models using neural networks](https://mlsanity.com/paper/2012.15339/) (2020)
- [Statistical Deep Learning for Spatial and Spatio-Temporal Data](https://mlsanity.com/paper/2206.02218/) (2022)
- [Advances in Statistical Modeling of Spatial Extremes](https://mlsanity.com/paper/2007.00774/) (2020)
- [Deep Gaussian Covariance Network](https://mlsanity.com/paper/1710.06202/) (2017)
- [Modeling Extremal Streamflow using Deep Learning Approximations and a Flexible Spatial Process](https://mlsanity.com/paper/2208.03344/) (2022)
- [Towards black-box parameter estimation](https://mlsanity.com/paper/2303.15041/) (2023)
- [INLA goes extreme: Bayesian tail regression for the estimation of high spatio-temporal quantiles](https://mlsanity.com/paper/1802.01085/) (2018)
- [Modelling extremes using approximate Bayesian Computation](https://mlsanity.com/paper/1411.1451/) (2014)
- [A flexible Bayesian hierarchical modeling framework for spatially dependent peaks-over-threshold data](https://mlsanity.com/paper/2112.09530/) (2021)

## Built on by (most cited first, 7 of 7)

- [Neural Methods for Amortized Inference](https://mlsanity.com/paper/2404.12484/) (2024)
- [Neural Bayes Estimators for Irregular Spatial Data using Graph Neural Networks](https://mlsanity.com/paper/2310.02600/) (2023)
- [Neural Bayes estimators for censored inference with peaks-over-threshold models](https://mlsanity.com/paper/2306.15642/) (2023)
- [Modeling Spatial Extremes using Non-Gaussian Spatial Autoregressive Models via Convolutional Neural Networks](https://mlsanity.com/paper/2505.03034/) (2025)
- [LatticeVision: Image to Image Networks for Modeling Non-Stationary Spatial Data](https://mlsanity.com/paper/2505.09803/) (2025)
- [Modeling Spatial Extremal Dependence of Precipitation Using Distributional Neural Networks](https://mlsanity.com/paper/2407.08668/) (2024)
- [Neural Bayes estimation and selection for complex bivariate extremal dependence models](https://mlsanity.com/paper/2503.23156/) (2025)

## Abstract (arXiv)

The heavy-tailed behavior of the generalized extreme-value distribution makes it a popular choice for modeling extreme events such as floods, droughts, heatwaves, wildfires, etc. However, estimating the distribution's parameters using conventional maximum likelihood methods can be computationally intensive, even for moderate-sized datasets. To overcome this limitation, we propose a computationally efficient, likelihood-free estimation method utilizing a neural network. Through an extensive simulation study, we demonstrate that the proposed neural network-based method provides Generalized Extreme Value (GEV) distribution parameter estimates with comparable accuracy to the conventional maximum likelihood method but with a significant computational speedup. To account for estimation uncertainty, we utilize parametric bootstrapping, which is inherent in the trained network. Finally, we apply this method to 1000-year annual maximum temperature data from the Community Climate System Model version 3 (CCSM3) across North America for three atmospheric concentrations: 289 ppm CO2 (pre-industrial), 700 ppm CO2 (future conditions), and 1400 ppm CO2, and compare the results with those obtained using the maximum likelihood approach.