Green’s matching: an efficient approach to parameter estimation in complex dynamic systems

dc.contributor.author

Tan, Jianbin

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Zhang, Guoyu

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Wang, Xueqin

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Huang, Hui

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Yao, Fang

dc.date.accessioned

2024-04-13T04:32:55Z

dc.date.available

2024-04-13T04:32:55Z

dc.description.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>

dc.identifier.issn

1369-7412

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1467-9868

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https://hdl.handle.net/10161/30492

dc.language

en

dc.publisher

Oxford University Press (OUP)

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Journal of the Royal Statistical Society Series B: Statistical Methodology

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10.1093/jrsssb/qkae031

dc.rights.uri

https://creativecommons.org/licenses/by-nc/4.0

dc.title

Green’s matching: an efficient approach to parameter estimation in complex dynamic systems

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Journal article

duke.contributor.orcid

Tan, Jianbin|0000-0002-3264-1086

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Duke

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School of Medicine

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Staff

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Basic Science Departments

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Biostatistics & Bioinformatics

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Biostatistics & Bioinformatics, Division of Translational Biomedical

pubs.publication-status

Published online

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