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Modelling with the Novel INAR(1)-PTE Process

Author

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  • Emrah Altun

    (Bartin University)

  • Naushad Mamode Khan

    (University of Mauritius)

Abstract

In this paper, the first-order non-negative integer-valued autoregressive process with Poisson-transmuted exponential innovations is introduced. Three estimation methods, namely, the conditional maximum likelihood, conditional least squares and Yule-Walker estimation methods are discussed to estimate the unknown parameters of the proposed process. Additionally, the simulation study is presented to assess the efficiencies of these estimation methods. Applications to two real-life data sets illustrate the usefulness of the proposed process.

Suggested Citation

  • Emrah Altun & Naushad Mamode Khan, 2022. "Modelling with the Novel INAR(1)-PTE Process," Methodology and Computing in Applied Probability, Springer, vol. 24(3), pages 1735-1751, September.
    • Handle: RePEc:spr:metcap:v:24:y:2022:i:3:d:10.1007_s11009-021-09878-2
    DOI: 10.1007/s11009-021-09878-2
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    References listed on IDEAS

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    1. Deepesh Bhati & Pooja Kumawat & E. Gómez–Déniz, 2017. "A new count model generated from mixed Poisson transmuted exponential family with an application to health care data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 46(22), pages 11060-11076, November.
    2. Schweer, Sebastian & Weiß, Christian H., 2014. "Compound Poisson INAR(1) processes: Stochastic properties and testing for overdispersion," Computational Statistics & Data Analysis, Elsevier, vol. 77(C), pages 267-284.
    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. Marcelo Bourguignon & Christian H. Weiß, 2017. "An INAR(1) process for modeling count time series with equidispersion, underdispersion and overdispersion," TEST: An Official Journal of the Spanish Society of Statistics and Operations Research, Springer;Sociedad de Estadística e Investigación Operativa, vol. 26(4), pages 847-868, December.
    5. 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.
    6. A. Alzaid & M. Al‐Osh, 1988. "First‐Order Integer‐Valued Autoregressive (INAR (1)) Process: Distributional and Regression Properties," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 42(1), pages 53-61, March.
    7. Ghitany, M.E. & Al-Mutairi, D.K. & Nadarajah, S., 2008. "Zero-truncated Poisson–Lindley distribution and its application," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(3), pages 279-287.
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    Cited by:

    1. 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.
    2. Radhakumari Maya & Christophe Chesneau & Anuresha Krishna & Muhammed Rasheed Irshad, 2022. "Poisson Extended Exponential Distribution with Associated INAR(1) Process and Applications," Stats, MDPI, vol. 5(3), pages 1-18, August.
    3. Muhammed Rasheed Irshad & Sreedeviamma Aswathy & Radhakumari Maya & Saralees Nadarajah, 2023. "New One-Parameter Over-Dispersed Discrete Distribution and Its Application to the Nonnegative Integer-Valued Autoregressive Model of Order One," Mathematics, MDPI, vol. 12(1), pages 1-14, December.
    4. Ané van der Merwe & Johannes T. Ferreira, 2022. "An Adapted Discrete Lindley Model Emanating from Negative Binomial Mixtures for Autoregressive Counts," Mathematics, MDPI, vol. 10(21), pages 1-21, November.

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