Parametric Multi-Fidelity Monte Carlo Estimation With Applications to Extremes
Abstract
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, for example, 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.
Fields of Study & Subjects
Reservoir Engineering and Simulation Methods
Monetary Policy and Economic Impact
Publication details
- Journal / source
- Technometrics
- Publisher
- Informa UK Limited
- Volume
- 68
- Issue
- 4
- Pages
- 1–14
- Publication date
- 21 May 2026
- Language
- EN