Neural networks for parameter estimation in intractable models
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....
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| Published in | Computational statistics & data analysis Vol. 185; no. C; p. 107762 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Netherlands
Elsevier B.V
01.09.2023
Elsevier |
| Subjects | |
| Online Access | Get full text |
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| 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. |
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| AbstractList | 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. |
| ArticleNumber | 107762 |
| Author | Lenzi, Amanda Stein, Michael L. Bessac, Julie Rudi, Johann |
| Author_xml | – sequence: 1 givenname: Amanda orcidid: 0000-0001-7618-7164 surname: Lenzi fullname: Lenzi, Amanda email: lenzi.amanda88@gmail.com organization: School of Mathematics, University of Edinburgh, United Kingdom – sequence: 2 givenname: Julie surname: Bessac fullname: Bessac, Julie organization: Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA – sequence: 3 givenname: Johann surname: Rudi fullname: Rudi, Johann organization: Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA – sequence: 4 givenname: Michael L. surname: Stein fullname: Stein, Michael L. organization: Department of Statistics, Rutgers University, Piscataway, NJ, USA |
| BackLink | https://www.osti.gov/biblio/2331353$$D View this record in Osti.gov |
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| Keywords | Parameter estimation Deep neural networks Max-stable distributions Intractable likelihood |
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| SubjectTerms | data analysis data collection Deep neural networks Intractable likelihood Max-stable distributions Parameter estimation statistics |
| Title | Neural networks for parameter estimation in intractable models |
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