Fast parameter estimation of Generalized Extreme Value distribution using Neural Networks

• URL: http://arxiv.org/abs/2305.04341v1
• Date: Sun, 7 May 2023 17:40:52 GMT
• Title: Fast parameter estimation of Generalized Extreme Value distribution using Neural Networks
• Authors: Sweta Rai, Alexis Hoffman, Soumendra Lahiri, Douglas W. Nychka, Stephan R. Sain, Soutir Bandyopadhyay
• Abstract summary: Generalized extreme-value distribution is a popular choice for modeling extreme events such as floods, droughts, heatwaves, wildfires, etc. We propose a computationally efficient, likelihood-free estimation method utilizing a neural network. We show that the proposed neural network-based method provides Generalized Extreme Value (GEV) distribution parameter estimates with comparable accuracy to the conventional maximum likelihood method.
• Score: 9.987055028382876
• License: http://creativecommons.org/licenses/by/4.0/
• 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, 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 $\mathrm{CO}_2$ (pre-industrial), 700 ppm $\mathrm{CO}_2$ (future conditions), and 1400 ppm $\mathrm{CO}_2$, and compare the results with those obtained using the maximum likelihood approach.

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