TY  - JOUR
Y1  - 2017///
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85042188211&doi=10.1155%2f2017%2f6148934&partnerID=40&md5=9568e7daabd10ab4739960792c2bfd9a
AV  - none
JF  - Complexity
PB  - Hindawi Limited
A1  - Mahmoud, A.A.
A1  - Dass, S.C.
A1  - Muthuvalu, M.S.
A1  - Asirvadam, V.S.
N2  - 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. © 2017 Ahmed A. Mahmoud et al.
VL  - 2017
ID  - scholars9072
SN  - 10762787
TI  - Maximum Likelihood Inference for Univariate Delay Differential Equation Models with Multiple Delays
N1  - cited By 2
ER  -