Parameter estimation and forecasting with quantified uncertainty for ordinary differential equation models using \textit{QuantDiffForecast}: a MATLAB toolbox and tutorial
From MaRDI portal
Publication:6618474
DOI10.1002/SIM.10036zbMATH Open1545.62272MaRDI QIDQ6618474
Gerardo Chowell, Amanda Bleichrodt, Ruiyan Luo
Publication date: 14 October 2024
Published in: Statistics in Medicine (Search for Journal in Brave)
parameter estimationordinary differential equationstutorialODE model toolboxreal-time forecasting and performance
Cites Work
- Title not available (Why is that?)
- \(\sqrt{n}\)-consistent parameter estimation for systems of ordinary differential equations: bypassing numerical integration via smoothing
- Finding identifiable parameter combinations in nonlinear ODE models and the rational reparameterization of their input-output equations
- Asymptotic theory of nonlinear least squares estimation
- Tutorial on maximum likelihood estimation
- Behind and beyond the MATLAB ODE suite
- Comparative assessment of parameter estimation methods in the presence of overdispersion: a simulation study
- Transmission dynamics of the great influenza pandemic of 1918 in Geneva, Switzerland: assessing the effects of hypothetical interventions
- Modeling and inverse problems in the presence of uncertainty
- Applied Predictive Modeling
- On Identifiability of Nonlinear ODE Models and Applications in Viral Dynamics
- Mathematical Models in Population Biology and Epidemiology
- The MATLAB ODE Suite
- Regression and time series model selection in small samples
- Further analysis of the data by Akaike's information criterion and the finite corrections
- Parameter Estimation for Differential Equations: a Generalized Smoothing Approach
- On iteratively regularized predictor–corrector algorithm for parameter identification *
- Quantitative Methods for Investigating Infectious Disease Outbreaks
- Strictly Proper Scoring Rules, Prediction, and Estimation
- Parameter Estimation for Differential Equation Models Using a Framework of Measurement Error in Regression Models
- Numerical Methods for Ordinary Differential Equations
- The elements of statistical learning. Data mining, inference, and prediction
- Bayesian workflow for disease transmission modeling in Stan
This page was built for publication: Parameter estimation and forecasting with quantified uncertainty for ordinary differential equation models using \textit{QuantDiffForecast}: a MATLAB toolbox and tutorial
Report a bug (only for logged in users!)Click here to report a bug for this page (MaRDI item Q6618474)