Numerical Discretization-Based Estimation Methods for Ordinary Differential Equation Models via Penalized Spline Smoothing with Applications in Biomedical Research

DOI10.1111/j.1541-0420.2012.01752.xzbMath1274.62906OpenAlexW2098515605WikidataQ41195024 ScholiaQ41195024MaRDI QIDQ2912324

Hulin Wu, Hongqi Xue, Arun Kumar

Publication date: 14 September 2012

Published in: Biometrics (Search for Journal in Brave)

Full work available at URL: http://europepmc.org/articles/pmc3496749

zbMATH Keywords

ordinary differential equationRunge-Kutta methodpenalized splinestrapezoidal ruleHIV dynamicsEuler's methodtwo-stage smoothing method

Mathematics Subject Classification ID

Related Items (15)

Two-step Bayesian methods for generalized regression driven by partial differential equations ⋮ Robust estimation of constant and time-varying parameters in nonlinear ordinary differential equation models ⋮ Kernel-Based Profile Estimation for Ordinary Differential Equations with Partially Measured State Variables ⋮ Parameter estimation for semiparametric ordinary differential equation models ⋮ Inference for differential equation models using relaxation via dynamical systems ⋮ Robust estimation of parameters in nonlinear ordinary differential equation models ⋮ Detection of influential measurement for ordinary differential equation with application to HIV dynamics ⋮ Multiple shooting-local linearization method for the identification of dynamical systems ⋮ Bayesian two-step estimation in differential equation models ⋮ A refined parameter estimating approach for HIV dynamic model ⋮ Numerical discretization-based kernel type estimation methods for ordinary differential equation models ⋮ Goodness of fit in nonlinear dynamics: misspecified rates or misspecified states? ⋮ Sparse Additive Ordinary Differential Equations for Dynamic Gene Regulatory Network Modeling ⋮ Generalized Ordinary Differential Equation Models ⋮ A Galerkin discretisation-based identification for parameters in nonlinear mechanical systems

Uses Software

• SemiPar

Cites Work

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