Bias and Coverage Properties of the WENDy-IRLS Algorithm
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arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909823638962176 |
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| author | Chawla, Abhi Bortz, David M. Dukic, Vanja |
| author_facet | Chawla, Abhi Bortz, David M. Dukic, Vanja |
| contents | The Weak form Estimation of Nonlinear Dynamics (WENDy) method is a recently proposed class of parameter estimation algorithms that exhibits notable noise robustness and computational efficiency. This work examines the coverage and bias properties of the original WENDy-IRLS algorithm's parameter and state estimators in the context of the following differential equations: Logistic, Lotka-Volterra, FitzHugh-Nagumo, Hindmarsh-Rose, and a Protein Transduction Benchmark. The estimators' performance was studied in simulated data examples, under four different noise distributions (normal, log-normal, additive censored normal, and additive truncated normal), and a wide range of noise, reaching levels much higher than previously tested for this algorithm. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_03365 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bias and Coverage Properties of the WENDy-IRLS Algorithm Chawla, Abhi Bortz, David M. Dukic, Vanja Methodology Machine Learning 62FXX, 62JFXX, 65L09 The Weak form Estimation of Nonlinear Dynamics (WENDy) method is a recently proposed class of parameter estimation algorithms that exhibits notable noise robustness and computational efficiency. This work examines the coverage and bias properties of the original WENDy-IRLS algorithm's parameter and state estimators in the context of the following differential equations: Logistic, Lotka-Volterra, FitzHugh-Nagumo, Hindmarsh-Rose, and a Protein Transduction Benchmark. The estimators' performance was studied in simulated data examples, under four different noise distributions (normal, log-normal, additive censored normal, and additive truncated normal), and a wide range of noise, reaching levels much higher than previously tested for this algorithm. |
| title | Bias and Coverage Properties of the WENDy-IRLS Algorithm |
| topic | Methodology Machine Learning 62FXX, 62JFXX, 65L09 |
| url | https://arxiv.org/abs/2510.03365 |