A Generalized Smoother for Linear Ordinary Differential Equations. Issue 3 (3rd July 2017)

• Record Type: Journal Article
• Authors: Carey, Michelle; Gath, Eugene G.; Hayes, Kevin
• Is Part Of: Journal of computational and graphical statistics. Volume 26:Issue 3(2017)
• Page Start: 671
• Page End: 681
• Publication Date: 2017-07-03
• Subjects: B-splines -- Functional data analysis -- Model-based penalized regression; Mathematical statistics -- Data processing -- Periodicals; Mathematical statistics -- Graphic methods -- Periodicals; 519.50285
• DOI: 10.1080/10618600.2016.1265526
• Languages: English
• ISSNs: 1061-8600

Abstract

ABSTRACT: Ordinary differential equations (ODEs) are equalities involving a function and its derivatives that define the evolution of the function over a prespecified domain. The applications of ODEs range from simulation and prediction to control and diagnosis in diverse fields such as engineering, physics, medicine, and finance. Parameter estimation is often required to calibrate these theoretical models to data. While there are many methods for estimating ODE parameters from partially observed data, they are invariably subject to several problems including high computational cost, complex estimation procedures, biased estimates, and large sampling variance. We propose a method that overcomes these issues and produces estimates of the ODE parameters that have less bias, a smaller sampling variance, and a 10-fold improvement in computational efficiency. The package GenPen containing the Matlab code to perform the methods described in this article is available online.