Asymptotic Properties of Parameter Estimation in Ordinary Differential Equations by Generalized Profiling Approach

• Activity Number: 427
• Type: Contributed
• Date/Time: Tuesday, August 3, 2010 : 2:00 PM to 3:50 PM
• Sponsor: IMS
• Author(s): Peisi Yan*+ and Harrison Huibin Zhou
• Companies: Yale University and Yale University
• Keywords: Parameter estimation ; dynamic system ; asymptotic

Abstract

Parameter estimation for differential equations from noisy data arises in many fields of science and engineering with applications ranging from small scale industrial processes to the future global climate evolution. Traditional methods are not only computationally intensive but also often inaccurate for statistical inference. Ramsay et al. (2007) proposed a generalized profiling approach which express the approximation of ODE solution in terms of a basis function expansion through a penalized data-smoothing scheme and later estimate parameters by a standard nonlinear data fitting procedure based on the approximate ODE solution. However, the statistical properties of this approach are not known. In this talk, we will give a theoretical justification for their generalized profiling approach.