A Monte Carlo Based Analysis of Optimal Design Criteria
NORTH CAROLINA STATE UNIV AT RALEIGH CENTER FOR RESEARCH IN SCIENTIFIC COMPUTATION
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Optimal design methods designed to choose optimal sampling distributions through minimization of a specific cost function related to the resulting error in parameter estimates for inverse or parameter estimation problems are considered. We compare a recent design criteria SE-optimal design standard error optimal design with the more traditional D-optimal and E-optimal designs. The optimal sampling distributions from each design are used to compute and compare standard errors here the standard errors for parameters are computed using the optimal mesh along with Monte Carlo simulations as compared to asymptotic theory based standard errors. We illustrate ideas with two examples the Verhulst-Pearl logistic population model and the standard harmonic oscillator model.
- Statistics and Probability
- Operations Research