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Improved near-exact distributions for the product of independent Generalized Gamma random variables

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  • Marques, Filipe J.
  • Loingeville, Florence

Abstract

The Generalized Gamma distribution is an important distribution in Statistics since it has as particular cases many well known and important distributions and also due to its very interesting modeling properties, which makes it an attractive tool. The distribution of the product of independent Generalized Gamma distributions is investigated. Most of the results available for this distribution are based on Meijer-G or H functions which may still be very difficult to handle. Therefore, near-exact distributions which are based on the Generalized Near-Integer Gamma distribution and which have density and cumulative distribution functions easily implementable and computationally appealing are developed. Numerical studies with computationally intensive analyses are carried out to study the accuracy of these approximations in different scenarios. Also computational modules are provided for the implementation of these approximations. Finally, an example of application to quality control in microbiology is provided.

Suggested Citation

  • Marques, Filipe J. & Loingeville, Florence, 2016. "Improved near-exact distributions for the product of independent Generalized Gamma random variables," Computational Statistics & Data Analysis, Elsevier, vol. 102(C), pages 55-66.
    • Handle: RePEc:eee:csdana:v:102:y:2016:i:c:p:55-66
    DOI: 10.1016/j.csda.2016.04.004
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    References listed on IDEAS

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    1. Filipe Marques & Carlos Coelho & Barry Arnold, 2011. "A general near-exact distribution theory for the most common likelihood ratio test statistics used in Multivariate Analysis," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 20(1), pages 180-203, May.
    2. Carlos A. Coelho & Barry C. Arnold, 2014. "On the Exact and Near-Exact Distributions of the Product of Generalized Gamma Random Variables and the Generalized Variance," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 43(10-12), pages 2007-2033, May.
    3. Gomes, O. & Combes, C. & Dussauchoy, A., 2008. "Parameter estimation of the generalized gamma distribution," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(4), pages 955-963.
    4. Coelho, Carlos A., 1998. "The Generalized Integer Gamma Distribution--A Basis for Distributions in Multivariate Statistics," Journal of Multivariate Analysis, Elsevier, vol. 64(1), pages 86-102, January.
    5. Coelho, Carlos A., 2004. "The generalized near-integer Gamma distribution: a basis for 'near-exact' approximations to the distribution of statistics which are the product of an odd number of independent Beta random variables," Journal of Multivariate Analysis, Elsevier, vol. 89(2), pages 191-218, May.
    6. Burda, Martin & Harding, Matthew & Hausman, Jerry, 2012. "A Poisson mixture model of discrete choice," Journal of Econometrics, Elsevier, vol. 166(2), pages 184-203.
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    Cited by:

    1. Denys Pommeret & Laurence Reboul, 2019. "Approximating the Probability Density Function of a Transformation of Random Variables," Methodology and Computing in Applied Probability, Springer, vol. 21(2), pages 633-645, June.

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