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Convergence and inference for mixed Poisson random sums

Author

Listed:
  • Gabriela Oliveira

    (Universidade Federal de Minas Gerais)

  • Wagner Barreto-Souza

    (Universidade Federal de Minas Gerais
    King Abdullah University of Science and Technology)

  • Roger W. C. Silva

    (Universidade Federal de Minas Gerais)

Abstract

We study the limit distribution of partial sums with a random number of terms following a class of mixed Poisson distributions. The resulting weak limit is a mixture between a normal distribution and an exponential family, which we call by normal exponential family (NEF) laws. A new stability concept is introduced and a relationship between $$\alpha $$ α -stable distributions and NEF laws is established. We propose the estimation of the NEF model parameters through the method of moments and also by the maximum likelihood method via an Expectation–Maximization algorithm. Monte Carlo simulation studies are addressed to check the performance of the proposed estimators, and an empirical illustration of the financial market is presented.

Suggested Citation

  • Gabriela Oliveira & Wagner Barreto-Souza & Roger W. C. Silva, 2021. "Convergence and inference for mixed Poisson random sums," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 84(5), pages 751-777, July.
    • Handle: RePEc:spr:metrik:v:84:y:2021:i:5:d:10.1007_s00184-020-00800-3
    DOI: 10.1007/s00184-020-00800-3
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    References listed on IDEAS

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    4. Kozubowski, Tomasz J. & Rachev, Svetlozar T., 1994. "The theory of geometric stable distributions and its use in modeling financial data," European Journal of Operational Research, Elsevier, vol. 74(2), pages 310-324, April.
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