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

Mohammadi Zohreh (), Bakouch Hassan S. () and Sharafi Maryam ()
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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

The International Journal of Biostatistics, 2023, vol. 19, issue 2, 473-488

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.

Keywords: cosine poisson distribution; COVID-19; dispersion; INAR process; predicted values (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1515/ijb-2022-0053

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