Generalized Ordinary Differential Equation Models

摘要: Existing estimation methods for ordinary differential equation (ODE) models are not applicable to discrete data. The generalized ODE (GODE) model is therefore proposed and investigated the first time. We develop likelihood-based parameter inference GODE models. propose robust computing algorithms rigorously investigate asymptotic properties of estimator by considering both measurement errors numerical in solving ODEs. simulation study application our an influenza viral dynamics suggest that have a superior performance terms accuracy over existing approach extended smoothing-based (ESB) method. Supplementary materials this article available online.