Parameter estimation of discretely observed interacting particle systems

08/25/2022
by   Chiara Amorino, et al.
0

In this paper, we consider the problem of joint parameter estimation for drift and diffusion coefficients of a stochastic McKean-Vlasov equation and for the associated system of interacting particles. The analysis is provided in a general framework, as both coefficients depend on the solution of the process and on the law of the solution itself. Starting from discrete observations of the interacting particle system over a fixed interval [0, T], we propose a contrast function based on a pseudo likelihood approach. We show that the associated estimator is consistent when the discretization step (Δ_n) and the number of particles (N) satisfy Δ_n → 0 and N →∞, and asymptotically normal when additionally the condition Δ_n N → 0 holds.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/25/2021

Parameter Estimation for the McKean-Vlasov Stochastic Differential Equation

In this paper, we consider the problem of parameter estimation for a sto...
research
05/07/2020

Parameter estimation for one-sided heavy-tailed distributions

Stable subordinators, and more general subordinators possessing power la...
research
03/01/2023

Density fluctuations in weakly interacting particle systems via the Dean-Kawasaki equation

The Dean-Kawasaki equation - one of the most fundamental SPDEs of fluctu...
research
03/23/2023

Interacting Particle Langevin Algorithm for Maximum Marginal Likelihood Estimation

We study a class of interacting particle systems for implementing a marg...
research
11/07/2020

Nonparametric estimation for interacting particle systems : McKean-Vlasov models

We consider a system of N interacting particles, governed by transport a...
research
06/21/2021

Variations on Hammersley's interacting particle process

The longest increasing subsequence problem for permutations has been stu...

Please sign up or login with your details

Forgot password? Click here to reset