Statistical modelling of COVID-19 and drug data via an INAR(1) process with a recent thinning operator and cosine Poisson innovations
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Zohreh Mohammadi
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.
Acknowledgment
The authors are very grateful to the Editor and the two anonymous referees for their encouragement and valuable comments that greatly improve the paper.
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Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
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Research funding: None declared.
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Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
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© 2022 Walter de Gruyter GmbH, Berlin/Boston
Articles in the same Issue
- Frontmatter
- Part-1: SMAC 2021 Webconference
- Statistics, philosophy, and health: the SMAC 2021 webconference
- Part-2: Regular Articles
- “Show me the DAG!”
- Causal inference for oncology: past developments and current challenges
- The EBM+ movement
- Bayesianism from a philosophical perspective and its application to medicine
- Bayesian inference for optimal dynamic treatment regimes in practice
- Agent-based modeling in medical research, virtual baseline generator and change in patients’ profile issue
- Agent based modeling in health care economics: examples in the field of thyroid cancer
- A copula-based set-variant association test for bivariate continuous, binary or mixed phenotypes
- Detection of atypical response trajectories in biomedical longitudinal databases
- Potential application of elastic nets for shared polygenicity detection with adapted threshold selection
- Error analysis of the PacBio sequencing CCS reads
- A SIMEX approach for meta-analysis of diagnostic accuracy studies with attention to ROC curves
- Statistical modelling of COVID-19 and drug data via an INAR(1) process with a recent thinning operator and cosine Poisson innovations
- The balanced discrete triplet Lindley model and its INAR(1) extension: properties and COVID-19 applications
Articles in the same Issue
- Frontmatter
- Part-1: SMAC 2021 Webconference
- Statistics, philosophy, and health: the SMAC 2021 webconference
- Part-2: Regular Articles
- “Show me the DAG!”
- Causal inference for oncology: past developments and current challenges
- The EBM+ movement
- Bayesianism from a philosophical perspective and its application to medicine
- Bayesian inference for optimal dynamic treatment regimes in practice
- Agent-based modeling in medical research, virtual baseline generator and change in patients’ profile issue
- Agent based modeling in health care economics: examples in the field of thyroid cancer
- A copula-based set-variant association test for bivariate continuous, binary or mixed phenotypes
- Detection of atypical response trajectories in biomedical longitudinal databases
- Potential application of elastic nets for shared polygenicity detection with adapted threshold selection
- Error analysis of the PacBio sequencing CCS reads
- A SIMEX approach for meta-analysis of diagnostic accuracy studies with attention to ROC curves
- Statistical modelling of COVID-19 and drug data via an INAR(1) process with a recent thinning operator and cosine Poisson innovations
- The balanced discrete triplet Lindley model and its INAR(1) extension: properties and COVID-19 applications
