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Mathematical Biosciences and Engineering

2016, Volume 13, Issue 1: 67-82. doi: 10.3934/mbe.2016.13.67
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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
  • Received: 01 March 2015 Accepted: 29 June 2018 Published: 01 October 2015

  • MSC : 62k05.

  • 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.

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

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  • 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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  • This article has been cited by:

    1. H Dette, R Guchenko, V B Melas, W K Wong, Optimal discrimination designs for semiparametric models, 2018, 105, 0006-3444, 185, 10.1093/biomet/asx058
    2. Jesús López–Fidalgo, Chiara Tommasi, 2018, Chapter 24, 978-3-319-73847-5, 253, 10.1007/978-3-319-73848-2_24
    3. Zheng Gong, Xiaoyuan Zhu, Chongqi Zhang, D-optimal design of the additive mixture model with multi-response, 2022, 19, 1551-0018, 4737, 10.3934/mbe.2022221
    4. Jesús López-Fidalgo, 2023, Chapter 5, 978-3-031-35917-0, 111, 10.1007/978-3-031-35918-7_5
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