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Title: Parameter inference for partially observed linear SDEs with discrete observations Authors:  Masahiro Kurisaki - University of Tokyo (Japan) [presenting]
Abstract: A problem of parameter estimation is considered for the state space model described by linear stochastic differential equations. We assume that an unobservable Ornstein-Uhlenbeck process drives another observable process by the linear stochastic differential equation, and these two processes depend on some unknown parameters. We construct the quasi-likelihood estimator (QMLE) of the unknown parameters and show the asymptotic properties of the estimator. Moreover, the function of YUIMA to execute our estimation on R will be discussed.