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On the use of correlated beta random variables with animal population modelling

Author

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  • Dias, Carlos Tadeu dos Santos
  • Samaranayaka, Ari
  • Manly, Bryan

Abstract

We give reasons why demographic parameters such as survival and reproduction rates are often modelled well in stochastic population simulation using beta distributions. In practice, it is frequently expected that these parameters will be correlated, for example with survival rates for all age classes tending to be high or low in the same year. We therefore discuss a method for producing correlated beta random variables by transforming correlated normal random variables, and show how it can be applied in practice by means of a simple example. We also note how the same approach can be used to produce correlated uniform, triangular, and exponential random variables.

Suggested Citation

  • Dias, Carlos Tadeu dos Santos & Samaranayaka, Ari & Manly, Bryan, 2008. "On the use of correlated beta random variables with animal population modelling," Ecological Modelling, Elsevier, vol. 215(4), pages 293-300.
    • Handle: RePEc:eee:ecomod:v:215:y:2008:i:4:p:293-300
    DOI: 10.1016/j.ecolmodel.2008.03.020
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    Cited by:

    1. Laura Di Giorgio & Abraham D Flaxman & Mark W Moses & Nancy Fullman & Michael Hanlon & Ruben O Conner & Alexandra Wollum & Christopher J L Murray, 2016. "Efficiency of Health Care Production in Low-Resource Settings: A Monte-Carlo Simulation to Compare the Performance of Data Envelopment Analysis, Stochastic Distance Functions, and an Ensemble Model," PLOS ONE, Public Library of Science, vol. 11(1), pages 1-20, January.
    2. Samaranayaka, Ari & Fletcher, David, 2010. "Modelling environmental stochasticity in adult survival for a long-lived species," Ecological Modelling, Elsevier, vol. 221(3), pages 423-427.
    3. Mark Huber & Nevena Marić, 2019. "Admissible Bernoulli correlations," Journal of Statistical Distributions and Applications, Springer, vol. 6(1), pages 1-8, December.
    4. Jorge A. Sefair & Oscar Guaje & Andrés L. Medaglia, 2021. "A column-oriented optimization approach for the generation of correlated random vectors," OR Spectrum: Quantitative Approaches in Management, Springer;Gesellschaft für Operations Research e.V., vol. 43(3), pages 777-808, September.
    5. Löhndorf, Nils, 2016. "An empirical analysis of scenario generation methods for stochastic optimization," European Journal of Operational Research, Elsevier, vol. 255(1), pages 121-132.

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