title: Parameter Estimation of Partial Differential Equation Models.
abstract: Partial differential equation (PDE) models are commonly used to model complex dynamic systems in applied sciences such as biology and finance. The forms of these PDE models are usually proposed by experts based on their prior knowledge and understanding of the dynamic system. Parameters in PDE models often have interesting scientific interpretations, but their values are often unknown, and need to be estimated from the measurements of the dynamic system in the present of measurement errors. Most PDEs used in practice have no analytic solutions, and can only be solved with numerical methods. Currently, methods for estimating PDE parameters require repeatedly solving PDEs numerically under thousands of candidate parameter values, and thus the computational load is high. In this article, we propose two methods to estimate parameters in PDE models: a parameter cascading method and a Bayesian approach. In both methods, the underlying dynamic process modeled with the PDE model is represented via basis function expansion. For the parameter cascading method, we develop two nested levels of optimization to estimate the PDE parameters. For the Bayesian method, we develop a joint model for data and the PDE, and develop a novel hierarchical model allowing us to employ Markov chain Monte Carlo (MCMC) techniques to make posterior inference. Simulation studies show that the Bayesian method and parameter cascading method are comparable, and both outperform other available methods in terms of estimation accuracy. The two methods are demonstrated by estimating parameters in a PDE model from LIDAR data.
authors.affiliation: Beijing Novartis Pharma Co. Ltd., Pudong New District, Shanghai, 201203, China.
authors.forename: Xiaolei
authors.initials: X
authors.lastname: Xun
authors.affiliation: Department of Statistical and Actuarial Sciences, University of Western Ontario, London, ON, N6A5B7, Canada.
authors.forename: Jiguo
authors.initials: J
authors.lastname: Cao
authors.affiliation: Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX, 77843-3143.
authors.forename: Bani
authors.initials: B
authors.lastname: Mallick
authors.affiliation: Department of Statistics, Texas A&M University, 3143 TAMU, College Station, TX, 77843-3143.
authors.forename: Raymond J
authors.initials: RJ
authors.lastname: Carroll
authors.affiliation: Department of Statistics, North Carolina State University, Raleigh, North Carolina 27695.
authors.forename: Arnab
authors.initials: A
authors.lastname: Maity
keywords: Asymptotic theory; Basis function expansion; Bayesian method; Differential equations; Measurement error; Parameter cascading