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design [2012/05/07 07:43]
matt created
design [2012/05/07 08:24]
matt
Line 3: Line 3:
 ===== Literature ===== ===== Literature =====
  
 +**//​Penalized Nonlinear Least Squares Estimation of Time-Varying Parameters in Ordinary Differential Equations//​**\\
 +**Cao, Jiguo and Huang, Jianhua Z. and Wu, Hulin**\\
 +
 +Some quotes:
 +
 +> Classical optimal design criteria suffer from two major flaws when applied to nonlinear problems. First, they are based on linearizing the model around a point estimate of the unknown parameter and therefore depend on the uncertain value of that parameter. Second, classical design methods are unavailable in ill-posed estimation situations, where previous data lack the information needed to properly construct the design criteria...
 +
 +>The simulation-based approach allows one to start the model-based optimization of experiments at an early stage of the parameter estimation process, in situations where the classical design criteria are not available...
 +
 +Extends **Muller, P., Sanso, B., and De Iorio, M. (2004), “Optimal Bayesian Design by Inhomogeneous Markov Chain
 +Simulation,​” Journal of the American Statistical Association,​ 99 (467), 788–798**. This is "​Bayesian simulation-based optimal design"​.
 +
 +Use the term "​Experimental Map".
 +
 +<​code>​
 +@article{doi:​10.1198/​jcgs.2011.10021,​
 +  author = {Cao, Jiguo and Huang, Jianhua Z. and Wu, Hulin},
 +  title = {Penalized Nonlinear Least Squares Estimation of Time-Varying Parameters in Ordinary Differential Equations},
 +  journal = {Journal of Computational and Graphical Statistics},​
 +  volume = {21},
 +  number = {1},
 +  pages = {42-56},
 +  year = {2012},
 +  doi = {10.1198/​jcgs.2011.10021},​
 +  URL = {http://​amstat.tandfonline.com/​doi/​abs/​10.1198/​jcgs.2011.10021},​
 +  eprint = {http://​amstat.tandfonline.com/​doi/​pdf/​10.1198/​jcgs.2011.10021}
 +}
 +</​code>​
design.txt · Last modified: 2017/09/20 19:05 (external edit)