Bayesian Analysis of Mixed-effect Regression Models Driven by Ordinary Differential Equations
Author:
Publisher
Springer Science and Business Media LLC
Subject
Applied Mathematics,Statistics, Probability and Uncertainty,Statistics and Probability
Link
https://link.springer.com/content/pdf/10.1007/s13571-019-00199-6.pdf
Reference26 articles.
1. Bhaumik, P. and Ghosal, S. (2015). Bayesian two-step estimation in differential equation models. Electronic Journal of Statistics 9, 3124–3154.
2. Bhaumik, P. and Ghosal, S. (2017). Efficient Bayesian estimation and uncertainty quantification in ordinary differential equation models. Bernoulli 23, 3537–3570.
3. Bhaumik, P. and Ghosal, S. (2017). Bayesian inference for higher-order ordinary differential equation models. J. Multivar. Anal. 157, 103–114.
4. Brunel, N.J. (2008). Parameter estimation of ODEs via nonparametric estimators. Electronic Journal of Statistics 2, 1935–7524.
5. Brunel, N.J., Clairon, Q. and d’Alché-Buc, F. (2014). Parametric estimation of ordinary differential equations with orthogonality conditions. J. Am. Stat. Assoc. 109, 173–185.
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