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KL-optimal experimental design for discriminating between two growth models applied to a beef farm

1. Institute of Mathematics Applied to Science and Engineering, University of Castilla-La Mancha, 13071-Ciudad Real

The body mass growth of organisms is usually represented in terms of what is known as ontogenetic growth models, which represent the relation of dependence between the mass of the body and time. The paper is concerned with a problem of finding an optimal experimental design for discriminating between two competing mass growth models applied to a beef farm. T-optimality was first introduced for discrimination between models but in this paper, KL-optimality based on the Kullback-Leibler distance is used to deal with correlated obsevations since, in this case, observations on a particular animal are not independent.
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Keywords Discrimination between models; KL-optimality; Growth models; T-optimality.

Citation: Santiago Campos-Barreiro, Jesús López-Fidalgo. KL-optimal experimental design for discriminating between two growth models applied to a beef farm. Mathematical Biosciences and Engineering, 2016, 13(1): 67-82. doi: 10.3934/mbe.2016.13.67

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This article has been cited by

  • 1. H Dette, R Guchenko, V B Melas, W K Wong, Optimal discrimination designs for semiparametric models, Biometrika, 2018, 105, 1, 185, 10.1093/biomet/asx058
  • 2. Jesús López–Fidalgo, Chiara Tommasi, , The Mathematics of the Uncertain, 2018, Chapter 24, 253, 10.1007/978-3-319-73848-2_24

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Copyright Info: 2016, Santiago Campos-Barreiro, et al., licensee AIMS Press. This is an open access article distributed under the terms of the Creative Commons Attribution Licese (http://creativecommons.org/licenses/by/4.0)

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