Neural networks for parameter estimation in intractable models

Amanda Lenzi, Julie Bessac, Johann Rudi and Michael L. Stein

Computational Statistics & Data Analysis, 2023, vol. 185, issue C

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.

Keywords: Deep neural networks; Intractable likelihood; Max-stable distributions; Parameter estimation (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:csdana:v:185:y:2023:i:c:s0167947323000737

DOI: 10.1016/j.csda.2023.107762

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