Bayesian P-spline estimation in hierarchical models specified by systems of affine differential equations

DOI10.1177/1471082X12471371OpenAlexW2014295038MaRDI QIDQ4970798

Jonathan Jaeger, Philippe Lambert

Publication date: 7 October 2020

Published in: Statistical Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1177/1471082x12471371

zbMATH Keywords

parameter estimationordinary differential equationsdynamic systemsBayesian ODE-penalized B-splineODE model selection

Mathematics Subject Classification ID

Statistics (62-XX)

Related Items (4)

Parameter estimation and inference in dynamic systems described by linear partial differential equations ⋮ Bayesian estimation of time-varying parameters in ordinary differential equation models with noisy time-varying covariates ⋮ Bayesian penalized B-spline estimation approach for epidemic models ⋮ Bayesian penalized smoothing approaches in models specified using differential equations with unknown error distributions

Uses Software

• fda (R)

Cites Work

• Robust specification of the roughness penalty prior distribution in spatially adaptive Bayesian P-splines models
• Archimedean copula estimation using Bayesian splines smoothing techniques
• Flexible smoothing with \(B\)-splines and penalties. With comments and a rejoinder by the authors
• Functional data analysis.
• On Measuring and Correcting the Effects of Data Mining and Model Selection
• A Spline Least Squares Method for Numerical Parameter Estimation in Differential Equations
• Bayesian Measures of Model Complexity and Fit
• Parameter Estimation for Differential Equations: a Generalized Smoothing Approach
• A practical guide to splines.

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