Parametric multi-fidelity Monte Carlo estimation with applications to extremes Article Swipe

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Minji Kim , Brendan Brown , Vladas Pipiras ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2410.08523

In a multi-fidelity setting, data are available from two sources, high- and low-fidelity. Low-fidelity data has larger size and can be leveraged to make more efficient inference about quantities of interest, e.g. the mean, for high-fidelity variables. In this work, such multi-fidelity setting is studied when the goal is to fit more efficiently a parametric model to high-fidelity data. Three multi-fidelity parameter estimation methods are considered, joint maximum likelihood, (multi-fidelity) moment estimation and (multi-fidelity) marginal maximum likelihood, and are illustrated on several parametric models, with the focus on parametric families used in extreme value analysis. An application is also provided concerning quantification of occurrences of extreme ship motions generated by two computer codes of varying fidelity.

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Monte Carlo method Parametric statistics Fidelity Estimation Computer science Statistical physics Econometrics Parametric model Statistics Mathematics Engineering Physics Systems engineering Telecommunications

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2410.08523
PDF: https://arxiv.org/pdf/2410.08523
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Related Works: 10
OpenAlex ID: https://openalex.org/W4403444255

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DOI: https://doi.org/10.48550/arxiv.2410.08523

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Title: Parametric multi-fidelity Monte Carlo estimation with applications to extremes

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Language: en

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Publication year: 2024

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Publication date: 2024-10-11

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Authors: Minji Kim, Brendan Brown, Vladas Pipiras

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Landing page: https://arxiv.org/abs/2410.08523

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PDF URL: https://arxiv.org/pdf/2410.08523

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Concepts: Monte Carlo method, Parametric statistics, Fidelity, Estimation, Computer science, Statistical physics, Econometrics, Parametric model, Statistics, Mathematics, Engineering, Physics, Systems engineering, Telecommunications

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