Bias and Coverage Properties of the WENDy-IRLS Algorithm

Abhi Chawla, David M. Bortz and Vanja Dukic ()
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Abhi Chawla: University of Colorado, Department of Applied Mathematics
David M. Bortz: University of Colorado, Department of Applied Mathematics
Vanja Dukic: Department of Economics (courtesy), University of Colorado Boulder, Department of Applied Mathematics

A chapter in Handbook of Visual, Experimental and Computational Mathematics, 2026, pp 1639-1746 from Springer

Abstract: 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.

Keywords: Weak-form Estimation of Nonlinear Dynamics; Coverage; Bias; FitzHugh-Nagumo; Hindmarsh-Rose; and Protein Transduction Benchmark (search for similar items in EconPapers)
Date: 2026
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DOI: 10.1007/978-3-032-16368-4_86

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