Parameter Estimation for Partially Observed McKean-Vlasov Diffusions

Fuente: arXiv
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Autori principali: Jasra, Ajay, Maama, Mohamed, Tempone, Raul
Natura: Preprint
Pubblicazione: 2024
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author Jasra, Ajay
Maama, Mohamed
Tempone, Raul
author_facet Jasra, Ajay
Maama, Mohamed
Tempone, Raul
contents In this article we consider likelihood-based estimation of static parameters for a class of partially observed McKean-Vlasov (POMV) diffusion process with discrete-time observations over a fixed time interval. In particular, using the framework of [5] we develop a new randomized multilevel Monte Carlo method for estimating the parameters, based upon Markovian stochastic approximation methodology. New Markov chain Monte Carlo algorithms for the POMV model are introduced facilitating the application of [5]. We prove, under assumptions, that the expectation of our estimator is biased, but with expected small and controllable bias. Our approach is implemented on several examples.
format Preprint
id arxiv_https___arxiv_org_abs_2411_06716
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle Parameter Estimation for Partially Observed McKean-Vlasov Diffusions
Jasra, Ajay
Maama, Mohamed
Tempone, Raul
Methodology
Numerical Analysis
In this article we consider likelihood-based estimation of static parameters for a class of partially observed McKean-Vlasov (POMV) diffusion process with discrete-time observations over a fixed time interval. In particular, using the framework of [5] we develop a new randomized multilevel Monte Carlo method for estimating the parameters, based upon Markovian stochastic approximation methodology. New Markov chain Monte Carlo algorithms for the POMV model are introduced facilitating the application of [5]. We prove, under assumptions, that the expectation of our estimator is biased, but with expected small and controllable bias. Our approach is implemented on several examples.
title Parameter Estimation for Partially Observed McKean-Vlasov Diffusions
topic Methodology
Numerical Analysis
url https://arxiv.org/abs/2411.06716