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Neural networks for parameter estimation in intractable models

Author

Listed:
  • Lenzi, Amanda
  • Bessac, Julie
  • Rudi, Johann
  • Stein, Michael L.

Abstract

The goal is to use deep learning models to estimate parameters in statistical models when standard likelihood estimation methods are computationally infeasible. For instance, inference for max-stable processes is exceptionally challenging even with small datasets, but simulation is straightforward. Data from model simulations are used to train deep neural networks and learn statistical parameters from max-stable models. The proposed neural network-based method provides a competitive alternative to current approaches, as demonstrated by considerable accuracy and computational time improvements. It serves as a proof of concept for deep learning in statistical parameter estimation and can be extended to other estimation problems.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:csdana:v:185:y:2023:i:c:s0167947323000737
    DOI: 10.1016/j.csda.2023.107762
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    Citations

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    Cited by:

    1. Zhou, Houlin & Zhu, Hanbing & Wang, Xuejun, 2024. "Change point detection via feedforward neural networks with theoretical guarantees," Computational Statistics & Data Analysis, Elsevier, vol. 193(C).
    2. 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.

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