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Statistical modelling of COVID-19 and drug data via an INAR(1) process with a recent thinning operator and cosine Poisson innovations

Author

Listed:
  • Mohammadi Zohreh

    (Department of Statistics, Jahrom University, Jahrom, Iran)

  • Bakouch Hassan S.

    (Department of Mathematics, College of Science, Qassim University, Buraydah, Saudi Arabia)

  • Sharafi Maryam

    (Department of Statistics, Shiraz University, Shiraz, Iran)

Abstract

In this paper, we propose the first-order stationary integer-valued autoregressive process with the cosine Poisson innovation, based on the negative binomial thinning operator. It can be equi-dispersed, under-dispersed and over-dispersed. Therefore, it is flexible for modelling integer-valued time series. Some statistical properties of the process are derived. The parameters of the process are estimated by two methods of estimation and the performances of the estimators are evaluated via some simulation studies. Finally, we demonstrate the usefulness of the proposed model by modelling and analyzing some practical count time series data on the daily deaths of COVID-19 and the drug calls data.

Suggested Citation

  • Mohammadi Zohreh & Bakouch Hassan S. & Sharafi Maryam, 2023. "Statistical modelling of COVID-19 and drug data via an INAR(1) process with a recent thinning operator and cosine Poisson innovations," The International Journal of Biostatistics, De Gruyter, vol. 19(2), pages 473-488, November.
    • Handle: RePEc:bpj:ijbist:v:19:y:2023:i:2:p:473-488:n:10
    DOI: 10.1515/ijb-2022-0053
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