Parameter Estimation for Weakly Interacting Hypoelliptic Diffusions
Yuga Iguchi, Alexandros Beskos, Grigorios A. Pavliotis

TL;DR
This paper introduces a novel locally Gaussian approximation method for parameter estimation in weakly interacting hypoelliptic particle systems, enabling flexible inference even with partial observations and overcoming limitations of previous Euler-Maruyama-based approaches.
Contribution
It develops a new likelihood-based inference method for hypoelliptic IPSs that handles degenerate noise structures and partial data, extending beyond existing Euler-Maruyama schemes.
Findings
Asymptotic normality of the estimator as particle number and observations grow
Effective inference with partial observations using the proposed approximation
Numerical experiments demonstrate practical applicability and robustness
Abstract
We study parameter estimation for interacting particle systems (IPSs) consisting of weakly interacting multivariate hypoelliptic SDEs. We propose a locally Gaussian approximation of the transition dynamics, carefully designed to address the degenerate structure of the noise (diffusion matrix), thus leading to the formation of a well-defined full likelihood. Our approach permits carrying out statistical inference for a wide class of hypoelliptic IPSs that are not covered by recent works as the latter rely on the Euler-Maruyama scheme. We analyze a contrast estimator based on the developed likelihood with high-frequency particle observations over a fixed period and show its asymptotic normality as with a requirement that the step-size is such that , assuming that all particle coordinates (e.g.~position and…
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