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design [2012/05/07 08:24]
matt
design [2017/09/20 19:05]
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-====== Optimal Design of Experiments ====== 
  
-===== 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... 
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-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)