Monday, June 30th 2014
New ideas in the inference of ODEs
This talk is based on some of the results that are contained in Chapters 4,5 and 6 of my thesis. In many applications obtaining ordinary differential equation descriptions of dynamic processes is scientifically important. In both, Bayesian and likelihood approaches for estimating parameters of ordinary differential equations, the speed and the convergence of the estimation procedure may crucially depend on the choice of initial values of the parameters. Extending previous work, I will show how using window smoothing yields a fast estimator for systems that are linear in the parameters. Using weak assumptions on the measurement error, it can be shown that the proposed estimator is sqrt(n) -consistent. The estimator does not require an initial guess for the parameters and is computationally fast and, therefore, it can serve as a good initial estimate for more efficient estimators. The performance of the estimator is illustrated in simulation study.
Colloquium coordinators are Prof.dr. A.C.D. van Enter (e-mail : A.C.D.van.Enter@rug.nl) and
Dr. A.V. Kiselev (e-mail:
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