Estimation in Interacting Diffusions: Continuous and Discrete Sampling

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Applied Mathematics

ISSN Print: 2152-7385
ISSN Online: 2152-7393
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E-mail: am@scirp.org

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"Estimation in Interacting Diffusions: Continuous and Discrete Sampling"
written by Jaya Prakash Narayan Bishwal,
published by Applied Mathematics, Vol.2 No.9, 2011

has been cited by the following article(s):

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[1]	A method of moments estimator for interacting particle systems and their mean field limit
	SIAM/ASA Journal on Uncertainty Quantification, 2024

[2]	On nonparametric estimation of the interaction function in particle system models
	arXiv preprint arXiv:2402.14419, 2024

[3]	Bayesian Nonparametric Inference in McKean-Vlasov models
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[4]	Bernstein-von Mises Theorem and Bayes Estimation in Interacting Particle Systems of Diffusions
	European Journal of Statistics, 2023

[5]	Learning theory for inferring interaction kernels in second-order interacting agent systems
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[6]	Learning particle swarming models from data with Gaussian processes
	Mathematics of Computation, 2023

[7]	On the Identifiablility of Nonlocal Interaction Kernels in First-Order Systems of Interacting Particles on Riemannian Manifolds
	arXiv preprint arXiv:2305.12340, 2023

[8]	Online parameter estimation for the McKean–Vlasov stochastic differential equation
	Stochastic Processes and …, 2023

[9]	Semiparametric estimation of McKean–Vlasov SDEs
	Annales de l'Institut Henri …, 2023

[10]	The LAN property for McKean–Vlasov models in a mean-field regime
	Stochastic Processes and their Applications, 2023

[11]	Mean-field nonparametric estimation of interacting particle systems
	Conference on Learning Theory, 2022

[12]	Nonparametric adaptive estimation for interacting particle systems
	2022

[13]	MLE Evolution Equation for Fractional Diffusions and Berry-Esseen Inequality of Stochastic Gradient Descent Algorithm for American Option
	European Journal of Statistics, 2022

[14]	On the theory and applications of stochastic gradient descent in continuous time
	2022

[15]	Weak-form Sparse Identification of Differential Equations from Noisy Measurements
	2022

[16]	Parameter estimation of discretely observed interacting particle systems
	arXiv preprint arXiv …, 2022

[17]	INFERENCE FOR ERGODIC MCKEAN-VLASOV STOCHASTIC DIFFERENTIAL EQUATIONS WITH POLYNOMIAL INTERACTIONS
	Catalot, 2022

[18]	The LAN property for McKean-Vlasov models in a mean-field regime
	arXiv preprint arXiv:2205.05932, 2022

[19]	Filtered data and eigenfunction estimators for statistical inference of multiscale and interacting diffusion processes
	2022

[20]	Eigenfunction martingale estimators for interacting particle systems and their mean field limit
	arXiv preprint arXiv:2112.04870, 2021

[21]	Parameter Estimation for the McKean-Vlasov Stochastic Differential Equation
	2021

[22]	Probabilistic properties and parametric inference of small variance nonlinear self-stabilizing stochastic differential equations
	Catalot, C Laredo - Stochastic Processes and their Applications, 2021

[23]	Learning mean-field equations from particle data using wsindy
	arXiv preprint arXiv:2110.07756, 2021

[24]	Inference for large financial systems
	2020

[25]	LIKELIHOOD INFERENCE FOR LARGE FINANCIAL SYSTEMS
	2017

[26]	Parameter Estimation for Subdiffusions within Proteins in Nanoscale Biophysics

[1]	A Method of Moments Estimator for Interacting Particle Systems and their Mean Field Limit SIAM/ASA Journal on Uncertainty Quantification, 2024 DOI:10.1137/22M153848X

[2]	The LAN property for McKean–Vlasov models in a mean-field regime Stochastic Processes and their Applications, 2023 DOI:10.1016/j.spa.2022.10.002

[3]	Online parameter estimation for the McKean–Vlasov stochastic differential equation Stochastic Processes and their Applications, 2023 DOI:10.1016/j.spa.2023.05.002

[4]	Learning theory for inferring interaction kernels in second-order interacting agent systems Sampling Theory, Signal Processing, and Data Analysis, 2023 DOI:10.1007/s43670-023-00055-9

[5]	Parameter estimation of discretely observed interacting particle systems Stochastic Processes and their Applications, 2023 DOI:10.1016/j.spa.2023.06.011

[6]	The LAN property for McKean–Vlasov models in a mean-field regime Stochastic Processes and their Applications, 2023 DOI:10.1016/j.spa.2022.10.002

[7]	Semiparametric estimation of McKean–Vlasov SDEs Annales de l'Institut Henri Poincaré, Probabilités et Statistiques, 2023 DOI:10.1214/22-AIHP1261

[8]	Parameter estimation of discretely observed interacting particle systems Stochastic Processes and their Applications, 2023 DOI:10.1016/j.spa.2023.06.011

[9]	Learning particle swarming models from data with Gaussian processes Mathematics of Computation, 2023 DOI:10.1090/mcom/3915

[10]	Nonparametric adaptive estimation for interacting particle systems Scandinavian Journal of Statistics, 2023 DOI:10.1111/sjos.12661

[11]	Online parameter estimation for the McKean–Vlasov stochastic differential equation Stochastic Processes and their Applications, 2023 DOI:10.1016/j.spa.2023.05.002

[12]	The LAN property for McKean–Vlasov models in a mean-field regime Stochastic Processes and their Applications, 2023 DOI:10.1016/j.spa.2022.10.002

[13]	Online parameter estimation for the McKean–Vlasov stochastic differential equation Stochastic Processes and their Applications, 2023 DOI:10.1016/j.spa.2023.05.002

[14]	The LAN property for McKean–Vlasov models in a mean-field regime Stochastic Processes and their Applications, 2023 DOI:10.1016/j.spa.2022.10.002

[15]	Learning particle swarming models from data with Gaussian processes Mathematics of Computation, 2023 DOI:10.1090/mcom/3915

[16]	Eigenfunction Martingale Estimators for Interacting Particle Systems and Their Mean Field Limit SIAM Journal on Applied Dynamical Systems, 2022 DOI:10.1137/21M1464348

[17]	Learning mean-field equations from particle data using WSINDy Physica D: Nonlinear Phenomena, 2022 DOI:10.1016/j.physd.2022.133406

[18]	Probabilistic properties and parametric inference of small variance nonlinear self-stabilizing stochastic differential equations Stochastic Processes and their Applications, 2021 DOI:10.1016/j.spa.2021.09.002

[19]	Probabilistic properties and parametric inference of small variance nonlinear self-stabilizing stochastic differential equations Stochastic Processes and their Applications, 2021 DOI:10.1016/j.spa.2021.09.002

[20]	Inference for large financial systems Mathematical Finance, 2020 DOI:10.1111/mafi.12222

[21]	Inference for large financial systems Mathematical Finance, 2019 DOI:10.1111/mafi.12222

[22]	Inference for Large Financial Systems SSRN Electronic Journal , 2017 DOI:10.2139/ssrn.3012751

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