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

Author

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  • Ahmed A. Mahmoud
  • Sarat C. Dass
  • Mohana S. Muthuvalu
  • Vijanth S. Asirvadam

Abstract

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.

Suggested Citation

  • Ahmed A. Mahmoud & Sarat C. Dass & Mohana S. Muthuvalu & Vijanth S. Asirvadam, 2017. "Maximum Likelihood Inference for Univariate Delay Differential Equation Models with Multiple Delays," Complexity, Hindawi, vol. 2017, pages 1-14, October.
    • Handle: RePEc:hin:complx:6148934
    DOI: 10.1155/2017/6148934
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    References listed on IDEAS

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    1. Spanos,Aris, 1999. "Probability Theory and Statistical Inference," Cambridge Books, Cambridge University Press, number 9780521424080.
    2. Bellen, Alfredo & Zennaro, Marino, 2013. "Numerical Methods for Delay Differential Equations," OUP Catalogue, Oxford University Press, number 9780199671373.
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