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Efficient dependency models: Simulating dependent random variables

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  • Lamboni, Matieyendou

Abstract

Dependency functions of dependent variables are relevant for (i) performing uncertainty quantification and sensitivity analysis in presence of dependent variables and/or correlated variables, and (ii) simulating random dependent variables. In this paper, we mathematically derive practical dependency functions for classical multivariate distributions such as Dirichlet, elliptical distributions and independent uniform (resp. gamma and Gaussian) variables under constraints that are ready to be used. Since such dependency models are used for sampling random values and we have many dependency models for every joint cumulative distribution function, we provide a way for choosing the efficient sampling function using multivariate sensitivity analysis. We illustrate our approach by means of numerical simulations.

Suggested Citation

  • Lamboni, Matieyendou, 2022. "Efficient dependency models: Simulating dependent random variables," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 200(C), pages 199-217.
    • Handle: RePEc:eee:matcom:v:200:y:2022:i:c:p:199-217
    DOI: 10.1016/j.matcom.2022.04.018
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    References listed on IDEAS

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    1. Frédéric Vrins, 2018. "Sampling the Multivariate Standard Normal Distribution under a Weighted Sum Constraint," Risks, MDPI, vol. 6(3), pages 1-13, June.
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    5. McDonald, James B. & Xu, Yexiao J., 1995. "A generalization of the beta distribution with applications," Journal of Econometrics, Elsevier, vol. 66(1-2), pages 133-152.
    6. Lamboni, Matieyendou & Kucherenko, Sergei, 2021. "Multivariate sensitivity analysis and derivative-based global sensitivity measures with dependent variables," Reliability Engineering and System Safety, Elsevier, vol. 212(C).
    7. Kotz,Samuel & Nadarajah,Saralees, 2004. "Multivariate T-Distributions and Their Applications," Cambridge Books, Cambridge University Press, number 9780521826549.
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