{"ID":2860771,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2510.03365","arxiv_id":"2510.03365","title":"Bias and Coverage Properties of the WENDy-IRLS Algorithm","abstract":"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.","short_abstract":"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...","url_abs":"https://arxiv.org/abs/2510.03365","url_pdf":"https://arxiv.org/pdf/2510.03365v1","authors":"[\"Abhi Chawla\",\"David M. Bortz\",\"Vanja Dukic\"]","published":"2025-10-03T04:12:10Z","proceeding":"stat.ME","tasks":"[\"stat.ME\",\"cs.LG\",\"stat.ML\"]","methods":"[]","has_code":false}