A1090
Title: Adaptive Bayes estimator for stochastic differential equation with jumps under small noise asymptotics
Authors: Shuntaro Suzuki - Waseda University (Japan) [presenting]
Abstract: Parameter estimation for stochastic differential equations driven by Wiener processes and compound Poisson processes is considered. Unknown parameters are assumed, corresponding to coefficients of the drift term, diffusion term, and jump term, as well as the Poisson intensity and the probability density function of the underlying jump. Estimators based on adaptive Bayesian estimation from discrete observations are proposed. The consistency and asymptotic normality of the estimators is demonstrated within the framework of small noise asymptotics.
