CMStatistics 2018: Start Registration
View Submission - CMStatistics
Title: Application of one-step method to parameter estimation in ODE models Authors:  Itai Dattner - University of Haifa (Israel) [presenting]
Shota Gugushvili - Leiden University (Netherlands)
Abstract: An application of Le Cam one-step method to parameter estimation in ordinary differential equation models is presented. This computationally simple technique can serve as an alternative to numerical evaluation of the popular non-linear least squares estimator, which typically requires the use of an iterative algorithm and repetitive numerical integration of the ordinary differential equation system. The one-step method starts from a preliminary $\sqrt{n}$-consistent estimator of the parameter of interest and next turns it into an asymptotic (in the sample size $n$) equivalent of the least squares estimator through a numerically straightforward procedure. We demonstrate performance of the one-step estimator via extensive simulations and real data examples. The method enables the researcher to obtain both point and interval estimates. The preliminary $\sqrt{n}$-consistent estimator that we use depends on non-parametric smoothing, and we provide a data-driven methodology for choosing its tuning parameter and support it by theory. An easy implementation scheme of the one-step method for practical use is pointed out.