题目:Green's Matching: an Efficient Approach to Parameter Estimation in Complex Dynamic Systems
姓名:王学钦 讲席教授 (中国科学技术大学)
地点:致远楼108室
时间:2024年4月12日 星期五 10:00-11:00
Abstract:
Parameters of differential equations are essential to characterize the intrinsic behaviors of dynamic systems. 
Many scientific challenges are hindered by a lack of computational and statistical efficiency in parameter 
estimation of dynamic systems, especially for complex systems with general-order differential operators, such 
as motion dynamics. Aiming at discovering these dynamic systems behind noisy data, we develop a computationally 
tractable and statistically efficient two-step method called Green’s matching via estimating equations. 
Particularly, we avoid time-consuming numerical integration by the pre-smoothing of trajectories in the estimating 
equations, and the pre-smoothing of curve derivatives is generally not involved in the   estimating equations due 
to the inversion of differential operators by Green’s functions. These appealing features improve both computational 
and statistical efficiency for parameter estimation. We prove that Green’s matching attains statistically optimal 
convergence for general-order systems. While for the other two widely used two-step methods, their estimation biases 
may dominate the estimation errors, resulting in poor convergence rates for high-order systems. We conduct extensive 
simulations to examine the estimation behaviors of two-step methods and other competitive approaches. Our results 
show that Green’s matching outperforms other methods for parameter estimation, which also supports Green’s matching 
in more complicated statistical inferences, such as equation discovery or causal network inference, for general-order 
dynamic systems.
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