Green’s matching: an efficient approach to parameter estimation in complex dynamic systems
Abstract
<jats:title>Abstract</jats:title> <jats:p>Parameters of differential equations are essential to characterize intrinsic behaviours of dynamic systems. Numerous methods for estimating parameters in dynamic systems are computationally and/or statistically inadequate, especially for complex systems with general-order differential operators, such as motion dynamics. This article presents Green’s matching, a computationally tractable and statistically efficient two-step method, which only needs to approximate trajectories in dynamic systems but not their derivatives due to the inverse of differential operators by Green’s function. This yields a statistically optimal guarantee for parameter estimation in general-order equations, a feature not shared by existing methods, and provides an efficient framework for broad statistical inferences in complex dynamic systems.</jats:p>
Type
Department
Description
Provenance
Subjects
Citation
Permalink
Published Version (Please cite this version)
Publication Info
Tan, Jianbin, Guoyu Zhang, Xueqin Wang, Hui Huang and Fang Yao (n.d.). Green’s matching: an efficient approach to parameter estimation in complex dynamic systems. Journal of the Royal Statistical Society Series B: Statistical Methodology. 10.1093/jrsssb/qkae031 Retrieved from https://hdl.handle.net/10161/30492.
This is constructed from limited available data and may be imprecise. To cite this article, please review & use the official citation provided by the journal.
Collections
Scholars@Duke
Jianbin Tan
Unless otherwise indicated, scholarly articles published by Duke faculty members are made available here with a CC-BY-NC (Creative Commons Attribution Non-Commercial) license, as enabled by the Duke Open Access Policy. If you wish to use the materials in ways not already permitted under CC-BY-NC, please consult the copyright owner. Other materials are made available here through the author’s grant of a non-exclusive license to make their work openly accessible.