Application of one‐step method to parameter estimation in ODE models. (22nd February 2018)

• Record Type: Journal Article
• Authors: Dattner, Itai; Gugushvili, Shota
• Is Part Of: Statistica Neerlandica. Volume 72:Number 2(2018)
• Page Start: 126
• Page End: 156
• Publication Date: 2018-02-22
• Subjects: non‐linear least squares -- ordinary differential equations -- smooth and match estimator -- integral estimator -- Levenberg–Marquardt algorithm -- one‐step estimator.AMS 2000 classifications: Primary: 62F12 -- Secondary: 62G08 -- 62G20; Statistics -- Periodicals; 519.5; 314.92
• DOI: 10.1111/stan.12124
• Languages: English
• ISSNs: 0039-0402

Abstract

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.