NDLI: Estimating Parameters in Delay Differential Equation Models

Content Provider	Springer Nature Link
Author	Wang, Liangliang Cao, Jiguo
Copyright Year	2011
Abstract	Delay differential equations (DDEs) are widely used in ecology, physiology and many other areas of applied science. Although the form of the DDE model is usually proposed based on scientific understanding of the dynamic system, parameters in the DDE model are often unknown. Thus it is of great interest to estimate DDE parameters from noisy data. Since the DDE model does not usually have an analytic solution, and the numeric solution requires knowing the history of the dynamic process, the traditional likelihood method cannot be directly applied. We propose a semiparametric method to estimate DDE parameters. The key feature of the semiparametric method is the use of a flexible nonparametric function to represent the dynamic process. The nonparametric function is estimated by maximizing the DDE-defined penalized likelihood function. Simulation studies show that the semiparametric method gives satisfactory estimates of DDE parameters. The semiparametric method is demonstrated by estimating a DDE model from Nicholson’s blowfly population data.
Starting Page	68
Ending Page	83
Page Count	16
File Format	PDF
ISSN	10857117
Journal	Journal of Agricultural, Biological, and Environmental Statistics
Volume Number	17
Issue Number	1
e-ISSN	15372693
Language	English
Publisher	Springer-Verlag
Publisher Date	2011-10-05
Publisher Place	New York
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Environmental Monitoring/Analysis Biostatistics Agriculture Statistics for Life Sciences, Medicine, Health Sciences
Content Type	Text
Resource Type	Article
Subject	Applied Mathematics Statistics and Probability Environmental Science Agricultural and Biological Sciences Statistics, Probability and Uncertainty

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