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On the discrete analogue of the Teissier distribution and its associated INAR(1) process

Author

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  • Irshad, M.R.
  • Jodrá, P.
  • Krishna, A.
  • Maya, R.

Abstract

The continuous Teissier distribution was proposed by the French biologist Georges Teissier in 1934. This paper introduces the one-parameter discrete analogue distribution and studies some of its statistical properties, focussing the attention on the computer generation of pseudo-random data and the parameter estimation problem. More precisely, the new discrete distribution is suitable to deal with both underdispersed and overdispersed count data, the quantile function can be expressed in closed form in terms of the Lambert W function and the failure rate function is increasing. Monte Carlo simulation experiments revealed that estimation methods such as maximum likelihood, least squares and quantile least squares may produce estimates far away from the true parameter value. This drawback can be overcome due to the existence of an analytical expression for the quantile function and then accurate estimates are obtained by the maximum likelihood method even for small sample sizes. Additionally, the new distribution is also used to derive a first-order integer-valued autoregressive process. Different real data sets are used to illustrate that the proposed distribution provides a better fit than other alternative models and also outperforms other competitive processes when time series of counts are considered.

Suggested Citation

  • Irshad, M.R. & Jodrá, P. & Krishna, A. & Maya, R., 2023. "On the discrete analogue of the Teissier distribution and its associated INAR(1) process," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 214(C), pages 227-245.
  • Handle: RePEc:eee:matcom:v:214:y:2023:i:c:p:227-245
    DOI: 10.1016/j.matcom.2023.07.007
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    References listed on IDEAS

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    1. Subrata Chakraborty & Dhrubajyoti Chakravarty & Josmar Mazucheli & Wesley Bertoli, 2021. "A discrete analog of Gumbel distribution: properties, parameter estimation and applications," Journal of Applied Statistics, Taylor & Francis Journals, vol. 48(4), pages 712-737, March.
    2. M. El-Morshedy & M. S. Eliwa & H. Nagy, 2020. "A new two-parameter exponentiated discrete Lindley distribution: properties, estimation and applications," Journal of Applied Statistics, Taylor & Francis Journals, vol. 47(2), pages 354-375, January.
    3. Marcelo Bourguignon & Josemar Rodrigues & Manoel Santos-Neto, 2019. "Extended Poisson INAR(1) processes with equidispersion, underdispersion and overdispersion," Journal of Applied Statistics, Taylor & Francis Journals, vol. 46(1), pages 101-118, January.
    4. M. S. Eliwa & Ziyad Ali Alhussain & M. El-Morshedy, 2020. "Discrete Gompertz-G Family of Distributions for Over- and Under-Dispersed Data with Properties, Estimation, and Applications," Mathematics, MDPI, vol. 8(3), pages 1-26, March.
    5. Hassan Bakouch & Miroslav Ristić, 2010. "Zero truncated Poisson integer-valued AR(1) model," Metrika: International Journal for Theoretical and Applied Statistics, Springer, vol. 72(2), pages 265-280, September.
    6. Tito Lívio & Naushad Mamode Khan & Marcelo Bourguignon & Hassan S. Bakouch, 2018. "An INAR(1) model with Poisson-Lindley innovations," Economics Bulletin, AccessEcon, vol. 38(3), pages 1505-1513.
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