@article{fdi:010074828,
  title = {{F}itting multiple models to multiple data sets},
  author = {{M}arques, {G}. {M}. and {L}ika, {K}. and {A}ugustine, {S}. and {P}ecquerie, {L}aure and {K}ooijman, {S}. {A}. {L}. {M}.},
  editor = {},
  language = {{ENG}},
  abstract = {{D}ynamic {E}nergy {B}udget ({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. {I}n 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. {T}his problem is not specific to {DEB} models, and is (or should be) really general in biology. {W}e 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. {W}e here propose two much better symmetric candidates, that proved to work well in practice. {W}e illustrate estimation problems and their solutions with a {M}onte-{C}arlo case study for increasing amount of scatter, which decreased the amount of information in the data about one or more parameter values. {W}e here validate the method using a set of models with known parameters and different scatter structures. {W}e compare the loss functions on the basis of convergence, point and interval estimates. {W}e also discuss the use of pseudo-data, i.e. realistic values for parameters that we treat as data from which predictions can differ. {T}hese pseudo-data are used to avoid that a good fit results in parameter values that make no biological sense. {W}e discuss our new method for estimating confidence intervals and present a list of concrete recommendations for parameter estimation. {W}e conclude that the proposed method performs very well in recovering parameter values of a set of models, applied to a set of data. {T}his is consistent with our large-scale applications in practice.},
  keywords = {{L}oss function ; {P}arameter estimation ; {F}itting models ; {I}nterval estimates ; {P}oint estimates ; {M}onte {C}arlo simulation studies},
  booktitle = {},
  journal = {{J}ournal of {S}ea {R}esearch},
  volume = {143},
  numero = {{S}pecial {I}ssue},
  pages = {48--56},
  ISSN = {1385-1101},
  year = {2019},
  DOI = {10.1016/j.seares.2018.07.004},
  URL = {https://www.documentation.ird.fr/hor/fdi:010074828},
}