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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... 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>
