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Statistical Estimations for Non-Ergodic Vasicek Model Driven by Gaussian Processes | Synapse
March 3, 2026
Statistical Estimations for Non-Ergodic Vasicek Model Driven by Gaussian Processes
YC
Yong Chen
University of North Carolina at Chapel Hill
WG
Wu-Jun Gao
Shenzhen Technology University
YL
Ying Li
Xiangtan University
Key Points
The analysis presents statistical estimations for non-ergodic systems, enhancing predictive accuracy.
Key evidence shows that applying Gaussian processes improves the estimates significantly over traditional methods.
Assessment using a non-ergodic Vasicek model highlights the role of Gaussian processes in financial modeling.
These findings suggest further applications in finance and other fields, requiring validation in real scenarios.
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Chen et al. (Thu,) studied this question.
synapsesocial.com/papers/69a75e44c6e9836116a28b01
https://doi.org/https://doi.org/10.1007/s10255-026-0005-5