1. Fitting multiple models to multiple data sets
- Author
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Sebastiaan A.L.M. Kooijman, Starrlight Augustine, Gonçalo M. Marques, Konstadia Lika, Laure Pecquerie, Instituto Superior Técnico, Technical University of Lisbon, University of Crete [Heraklion] (UOC), Akvaplan-Niva [Tromsø], Norwegian Institute for Water Research (NIVA), Institut de Recherche pour le Développement (IRD), Laboratoire des Sciences de l'Environnement Marin (LEMAR) (LEMAR), Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Centre National de la Recherche Scientifique (CNRS)-Université de Brest (UBO), Vrije universiteit = Free university of Amsterdam [Amsterdam] (VU), Institut de Recherche pour le Développement (IRD)-Institut Français de Recherche pour l'Exploitation de la Mer (IFREMER)-Université de Brest (UBO)-Institut Universitaire Européen de la Mer (IUEM), Institut de Recherche pour le Développement (IRD)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Institut national des sciences de l'Univers (INSU - CNRS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Centre National de la Recherche Scientifique (CNRS), VU University Amsterdam, Molecular Cell Biology, AIMMS, and Theoretical Life Sciences
- Subjects
0106 biological sciences ,Context (language use) ,Monte Carlo simulation studies ,Interval (mathematics) ,Aquatic Science ,Oceanography ,010603 evolutionary biology ,01 natural sciences ,Point estimates ,Set (abstract data type) ,SDG 17 - Partnerships for the Goals ,Fitting models ,Convergence (routing) ,Parameter estimation ,Point estimation ,Ecology, Evolution, Behavior and Systematics ,parameters ,Basis (linear algebra) ,Estimation theory ,ACL ,010604 marine biology & hydrobiology ,Interval estimates ,Function (mathematics) ,Loss function ,covariation method ,[SDE.BE]Environmental Sciences/Biodiversity and Ecology ,Algorithm - Abstract
WOS:000453497600006; Dynamic Energy Budget (DEB) theory constitutes a coherent set of universal biological processes that have been used as building blocks for modeling biological systems over the last 40 years in many applied disciplines. In the context of extracting parameters for DEB models from data, we discuss the methodology of fitting multiple models, which share parameters, to multiple data sets in a single parameter estimation. This problem is not specific to DEB models, and is (or should be) really general in biology. We discovered that a lot of estimation problems that we suffered from in the past originated from the use of a loss function that was not symmetric in the role of data and predictions. We here propose two much better symmetric candidates, that proved to work well in practice. We illustrate estimation problems and their solutions with a Monte-Carlo case study for increasing amount of scatter, which decreased the amount of information in the data about one or more parameter values. We here validate the method using a set of models with known parameters and different scatter structures. We compare the loss functions on the basis of convergence, point and interval estimates. We also discuss the use of pseudo-data, i.e. realistic values for parameters that we treat as data from which predictions can differ. These pseudo-data are used to avoid that a good fit results in parameter values that make no biological sense. We discuss our new method for estimating confidence intervals and present a list of concrete recommendations for parameter estimation. We conclude that the proposed method performs very well in recovering parameter values of a set of models, applied to a set of data. This is consistent with our large-scale applications in practice.
- Published
- 2019
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