 Application of one†step method to parameter estimation in ODE modelsItai Dattner and Shota Gugushvili Statistica Neerlandica, 2018, vol. 72, issue 2, 126-156 Abstract: In this paper, we study application of Le Cam's one†step method to parameter estimation in ordinary differential equation models. 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 a multistep iterative algorithm and repetitive numerical integration of the ordinary differential equation system. The one†step method starts from a preliminary n†consistent estimator of the parameter of interest and next turns it into an asymptotic (as 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 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. Date: 2018 Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:bla:stanee:v:72:y:2018:i:2:p:126-156 Ordering information: This journal article can be ordered from Access Statistics for this article Statistica Neerlandica is currently edited by Miroslav Ristic, Marijtje van Duijn and Nan van Geloven More articles in Statistica Neerlandica from Netherlands Society for Statistics and Operations Research |
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