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Bivariate Poisson 2Sum-Lindley Distributions and the Associated BINAR(1) Processes

Author

Listed:
  • Muhammed Rasheed Irshad

    (Department of Statistics, Cochin University of Science and Technology, Cochin 682 022, Kerala, India)

  • Christophe Chesneau

    (Department of Mathematics (LMNO), University of Caen-Normandie, UFR de Sciences, F-14032 Caen, France)

  • Veena D’cruz

    (Department of Statistics, Cochin University of Science and Technology, Cochin 682 022, Kerala, India)

  • Naushad Mamode Khan

    (Department of Economics and Statistics, University of Mauritius, Réduit 80837, Mauritius)

  • Radhakumari Maya

    (Department of Statistics, University College, Trivandrum 695 014, Kerala, India)

Abstract

Discrete-valued time series modeling has witnessed numerous bivariate first-order integer-valued autoregressive process or BINAR(1) processes based on binomial thinning and different innovation distributions. These BINAR(1) processes are mainly focused on over-dispersion. This paper aims to propose new bivariate distributions and processes based on a recently proposed over-dispersed distribution: the Poisson 2S-Lindley distribution. The new bivariate distributions, denoted by the abbreviations BP2S-L(I) and BP2S-L(II), are then used as innovation distributions for the BINAR(1) process. Properties are investigated for both distributions as well as for the BINAR(1) processes. The distribution parameters are estimated using the maximum likelihood method, and the BINAR(1)BP2S-L(I) and BINAR(1)BP2S-L(II) process parameters are estimated using the conditional least squares and conditional maximum likelihood methods. Monte Carlo simulation experiments are conducted to study large and small sample performances and for the comparison of the estimation methods. The Pittsburgh crime series and candy sales datasets are then used to compare the new BINAR(1) processes to some other existing BINAR(1) processes in the literature.

Suggested Citation

  • Muhammed Rasheed Irshad & Christophe Chesneau & Veena D’cruz & Naushad Mamode Khan & Radhakumari Maya, 2022. "Bivariate Poisson 2Sum-Lindley Distributions and the Associated BINAR(1) Processes," Mathematics, MDPI, vol. 10(20), pages 1-24, October.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:20:p:3835-:d:944729
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    References listed on IDEAS

    as
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    3. R. K. Freeland & B. P. M. McCabe, 2004. "Analysis of low count time series data by poisson autoregression," Journal of Time Series Analysis, Wiley Blackwell, vol. 25(5), pages 701-722, September.
    4. Gómez-Déniz, Emilio & Sarabia, José María & Balakrishnan, N., 2012. "A Multivariate Discrete Poisson-Lindley Distribution: Extensions and Actuarial Applications," ASTIN Bulletin, Cambridge University Press, vol. 42(2), pages 655-678, November.
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    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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