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On generating multivariate Poisson data in management science applications

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  • Inbal Yahav
  • Galit Shmueli

Abstract

Generating multivariate Poisson random variables is essential in many applications, such as multi echelon supply chain systems, multi‐item/multi‐period pricing models, accident monitoring systems, etc. Current simulation methods suffer from limitations ranging from computational complexity to restrictions on the structure of the correlation matrix, and therefore are rarely used in management science. Instead, multivariate Poisson data are commonly approximated by either univariate Poisson or multivariate Normal data. However, these approximations are often not adequate in practice. In this paper, we propose a conceptually appealing correction for NORTA (NORmal To Anything) for generating multivariate Poisson data with a flexible correlation structure and rates. NORTA is based on simulating data from a multivariate Normal distribution and converting it into an arbitrary continuous distribution with a specific correlation matrix. We show that our method is both highly accurate and computationally efficient. We also show the managerial advantages of generating multivariate Poisson data over univariate Poisson or multivariate Normal data. Copyright © 2011 John Wiley & Sons, Ltd.

Suggested Citation

  • Inbal Yahav & Galit Shmueli, 2012. "On generating multivariate Poisson data in management science applications," Applied Stochastic Models in Business and Industry, John Wiley & Sons, vol. 28(1), pages 91-102, January.
    • Handle: RePEc:wly:apsmbi:v:28:y:2012:i:1:p:91-102
    DOI: 10.1002/asmb.901
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    Cited by:

    1. Bora Çekyay & J.B.G. Frenk & Sonya Javadi, 2023. "On Computing the Multivariate Poisson Probability Distribution," Methodology and Computing in Applied Probability, Springer, vol. 25(3), pages 1-22, September.
    2. Sergei Leonov & Bahjat Qaqish, 2020. "Correlated endpoints: simulation, modeling, and extreme correlations," Statistical Papers, Springer, vol. 61(2), pages 741-766, April.
    3. Lv, Jing & Yang, Hu & Guo, Chaohui, 2015. "An efficient and robust variable selection method for longitudinal generalized linear models," Computational Statistics & Data Analysis, Elsevier, vol. 82(C), pages 74-88.
    4. Modarres, Reza, 2016. "Multivariate Poisson interpoint distances," Statistics & Probability Letters, Elsevier, vol. 112(C), pages 113-123.

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