Neural networks for parameter estimation in intractable models

• Published in: Computational statistics & data analysis Vol. 185; no. C; p. 107762
• Main Authors: Lenzi, Amanda, Bessac, Julie, Rudi, Johann, Stein, Michael L.
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.09.2023; Elsevier
• Subjects: data analysis; data collection; Deep neural networks; Intractable likelihood; Max-stable distributions; Parameter estimation; statistics; Parameter estimation; Deep neural networks; Max-stable distributions; Intractable likelihood
• ISSN: 0167-9473; 1872-7352
• DOI: 10.1016/j.csda.2023.107762

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.