Maximum Likelihood Inference for Univariate Delay Differential Equation Models with Multiple Delays

• Published in: Complexity (New York, N.Y.) Vol. 2017; no. 2017; pp. 1 - 14
• Main Authors: Asirvadam, Vijanth S., Muthuvalu, Mohana S., Dass, Sarat C., Mahmoud, Ahmed A.
• Format: Journal Article
• Language: English
• Published: Cairo, Egypt Hindawi Publishing Corporation 01-01-2017; Hindawi; John Wiley & Sons, Inc; Hindawi Limited; Hindawi-Wiley
• Subjects: Adaptive algorithms; Applied mathematics; Biological computing; Biological systems; Computer simulation; Confidence intervals; Delay; Delay equations; Differential equations; Economic models; Estimates; Mathematical models; Matrix methods; Maximum likelihood estimators; Methodology; Newton-Raphson method; Parameter estimation; Parameters; Statistical analysis; Statistical inference; Theory; Trajectory analysis; United States
• DOI: 10.1155/2017/6148934
• EISSN: 1099-0526
• ISSN: 1076-2787

This article presents statistical inference methodology based on maximum likelihoods for delay differential equation models in the univariate setting. Maximum likelihood inference is obtained for single and multiple unknown delay parameters as well as other parameters of interest that govern the trajectories of the delay differential equation models. The maximum likelihood estimator is obtained based on adaptive grid and Newton-Raphson algorithms. Our methodology estimates correctly the delay parameters as well as other unknown parameters (such as the initial starting values) of the dynamical system based on simulation data. We also develop methodology to compute the information matrix and confidence intervals for all unknown parameters based on the likelihood inferential framework. We present three illustrative examples related to biological systems. The computations have been carried out with help of mathematical software: MATLAB® 8.0 R2014b.