Maximum Likelihood Theory for Large Interacting Systems

DOI10.1137/0150050zbMath0704.62074OpenAlexW2049296493MaRDI QIDQ3484226

R. A. Kasonga

Publication date: 1990

Published in: SIAM Journal on Applied Mathematics (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1137/0150050

zbMATH Keywords

consistencyasymptotic normalitymaximum likelihood estimatorinteracting particlesinteracting diffusionsItô equationslarge interacting systemsexchangeable subsystemsmean-field-type modeltesting of hypothesis of noninteraction

Mathematics Subject Classification ID

Asymptotic properties of parametric estimators (62F12) Markov processes: estimation; hidden Markov models (62M05) Stochastic ordinary differential equations (aspects of stochastic analysis) (60H10)

Related Items (13)

Eigenfunction Martingale Estimators for Interacting Particle Systems and Their Mean Field Limit ⋮ Learning mean-field equations from particle data using WSINDy ⋮ Learning theory for inferring interaction kernels in second-order interacting agent systems ⋮ Online parameter estimation for the McKean-Vlasov stochastic differential equation ⋮ Parameter estimation of discretely observed interacting particle systems ⋮ Nonparametric adaptive estimation for interacting particle systems ⋮ Semiparametric estimation of McKean-Vlasov SDEs ⋮ On inference for fractional differential equations ⋮ Identifiability of interaction kernels in mean-field equations of interacting particles ⋮ Probabilistic properties and parametric inference of small variance nonlinear self-stabilizing stochastic differential equations ⋮ Nonparametric estimation for interacting particle systems: McKean-Vlasov models ⋮ Parametric inference for small variance and long time horizon McKean-Vlasov diffusion models ⋮ The LAN property for McKean-Vlasov models in a mean-field regime

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