On the selection of ordinary differential equation models with application to predator-prey dynamical models.
Biometrics
; 71(1): 131-138, 2015 Mar.
Article
en En
| MEDLINE
| ID: mdl-25287611
ABSTRACT
We consider model selection and estimation in a context where there are competing ordinary differential equation (ODE) models, and all the models are special cases of a "full" model. We propose a computationally inexpensive approach that employs statistical estimation of the full model, followed by a combination of a least squares approximation (LSA) and the adaptive Lasso. We show the resulting method, here called the LSA method, to be an (asymptotically) oracle model selection method. The finite sample performance of the proposed LSA method is investigated with Monte Carlo simulations, in which we examine the percentage of selecting true ODE models, the efficiency of the parameter estimation compared to simply using the full and true models, and coverage probabilities of the estimated confidence intervals for ODE parameters, all of which have satisfactory performances. Our method is also demonstrated by selecting the best predator-prey ODE to model a lynx and hare population dynamical system among some well-known and biologically interpretable ODE models.
Palabras clave
Texto completo:
1
Banco de datos:
MEDLINE
Asunto principal:
Conducta Predatoria
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Conejos
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Modelos Estadísticos
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Biometría
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Lynx
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Modelos Biológicos
Tipo de estudio:
Prognostic_studies
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Risk_factors_studies
Límite:
Animals
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Humans
Idioma:
En
Año:
2015
Tipo del documento:
Article