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Fast parameter estimation of generalized extreme value distribution using neural networks

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  • Sweta Rai
  • Alexis Hoffman
  • Soumendra Lahiri
  • Douglas W. Nychka
  • Stephan R. Sain
  • Soutir Bandyopadhyay

Abstract

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 and so forth. 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 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 across North America for three atmospheric concentrations: 289 ppm CO2$$ {\mathrm{CO}}_2 $$ (pre‐industrial), 700 ppm CO2$$ {\mathrm{CO}}_2 $$ (future conditions), and 1400 ppm CO2$$ {\mathrm{CO}}_2 $$, and compare the results with those obtained using the maximum likelihood approach.

Suggested Citation

  • Sweta Rai & Alexis Hoffman & Soumendra Lahiri & Douglas W. Nychka & Stephan R. Sain & Soutir Bandyopadhyay, 2024. "Fast parameter estimation of generalized extreme value distribution using neural networks," Environmetrics, John Wiley & Sons, Ltd., vol. 35(3), May.
  • Handle: RePEc:wly:envmet:v:35:y:2024:i:3:n:e2845
    DOI: 10.1002/env.2845
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    References listed on IDEAS

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    1. Creel, Michael, 2017. "Neural nets for indirect inference," Econometrics and Statistics, Elsevier, vol. 2(C), pages 36-49.
    2. Milan Stojkovic & Slobodan P. Simonovic, 2019. "Mixed General Extreme Value Distribution for Estimation of Future Precipitation Quantiles Using a Weighted Ensemble - Case Study of the Lim River Basin (Serbia)," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 33(8), pages 2885-2906, June.
    3. Lenzi, Amanda & Bessac, Julie & Rudi, Johann & Stein, Michael L., 2023. "Neural networks for parameter estimation in intractable models," Computational Statistics & Data Analysis, Elsevier, vol. 185(C).
    4. Cooley, Daniel & Nychka, Douglas & Naveau, Philippe, 2007. "Bayesian Spatial Modeling of Extreme Precipitation Return Levels," Journal of the American Statistical Association, American Statistical Association, vol. 102, pages 824-840, September.
    5. Philémon Gamet & Jonathan Jalbert, 2022. "A flexible extended generalized Pareto distribution for tail estimation," Environmetrics, John Wiley & Sons, Ltd., vol. 33(6), September.
    6. Bucher, Axel & Segers, Johan, 2017. "On the maximum likelihood estimator for the Generalized Extreme-Value distribution," LIDAM Reprints ISBA 2017039, Université catholique de Louvain, Institute of Statistics, Biostatistics and Actuarial Sciences (ISBA).
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