Eigenfunction martingale estimators for interacting particle systems and their mean field limit
File(s)PZ_2022.pdf (2.89 MB)
Published version
Author(s)
Pavliotis, Grigorios A
Zanoni, Andrea
Type
Journal Article
Abstract
We study the problem of parameter estimation for large exchangeable interacting particle systems when a sample of discrete observations from a single particle is known. We propose a novel method based on martingale estimating functions constructed by employing the eigenvalues and eigenfunctions of the generator of the mean field limit, where the law of the process is replaced by the (unique) invariant measure of the mean field dynamics. We then prove that our estimator is asymptotically unbiased and asymptotically normal when the number of observations and the number of particles tend to infinity, and we provide a rate of convergence toward the exact value of the parameters. Finally, we present several numerical experiments which show the accuracy of our estimator and corroborate our theoretical findings, even in the case that the mean field dynamics exhibit more than one steady state.
Date Issued
2022-12-31
Date Acceptance
2022-06-11
Citation
SIAM Journal on Applied Dynamical Systems, 2022, 21 (4), pp.2338-2370
ISSN
1536-0040
Publisher
Society for Industrial and Applied Mathematics
Start Page
2338
End Page
2370
Journal / Book Title
SIAM Journal on Applied Dynamical Systems
Volume
21
Issue
4
Copyright Statement
© 2022 Society for Industrial and Applied Mathematics.
Publication Status
Published
Date Publish Online
2022-12-02