A0395
Title: A joint estimation approach to sparse additive ordinary differential equations
Authors: Nan Zhang - Fudan University (China) [presenting]
Muye Nanshan - Fudan University (China)
Jiguo Cao - Simon Fraser University (Canada)
Abstract: Ordinary differential equations (ODEs) are widely used to characterize the dynamics of complex systems in real applications. A novel joint estimation approach is proposed for generalized sparse additive ODEs where observations are allowed to be non-Gaussian. The new method is unified with existing collocation methods by simultaneously considering the likelihood, ODE fidelity and sparse regularization. A block coordinate descent algorithm is designed for optimizing the non-convex and non-differentiable objective function. The global convergence of the algorithm is established. The simulation study and two applications demonstrate the superior performance of the proposed method in estimation and improved performance of identifying the sparse structure.
