Parameter estimation in nonlinear multivariate stochastic differential equations based on splitting schemes
基于分裂格式的非线性多元随机微分方程参数估计
The likelihood functions for discretely observed nonlinear continuous time models based on stochastic differential equations are not available except for a few cases. Various parameter estimation techniques have been proposed, each with advantages, disadvantages and limitations depending on the application. Most applications still use the Euler–Maruyama discretization, despite many proofs of its bias. More sophisticated methods, such as Kessler’s Gaussian approximation,…