Parameter estimation of Ornstein-Uhlenbeck process generating a stochastic graph

Emmanuel Gobet (1) , Gustaw Matulewicz (1)

1 CMAP - Centre de Mathématiques Appliquées - Ecole Polytechnique

Given Y a graph process defined by an incomplete information observation of a multivariate Ornstein-Uhlenbeck process X, we investigate whether we can estimate the parameters of X. We define two statistics of Y. We prove convergence properties and show how these can be used for parameter inference. Finally, numerical tests illustrate our results and indicate possible extensions and applications.

Mots clés: stochastic graph process; inference for stochastic process; incomplete information; asymptotic properties of estimators

Domaines: Probabilités [math.PR]; Statistiques [stat]

Emmanuel Gobet, Gustaw Matulewicz. Parameter estimation of Ornstein-Uhlenbeck process generating a stochastic graph. Statistical Inference for Stochastic Processes, 2016, 20 (2), pp.211-235. ⟨10.1007/s11203-016-9142-4⟩. ⟨hal-01271994⟩