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A mixed INAR(p) model with serially dependent innovation with application to some COVID-19 data

Author

Listed:
  • Xiufang Liu
  • Wenzheng Yin
  • Wenkun Zhang
  • Huaping Chen

Abstract

This present work proposes a new mixed INAR(p) model (SDMINAR(p)) based on binomial thinning and negative binomial thinning operators, where the innovations are supposed to be serially dependent on the population at time {t−1,t−2,⋯,t−p}. Stationarity and ergodicity properties are given. Conditional least squares and weighted conditional least squares are adopted to estimate the model parameters. The asymptotic normality property of the estimators is presented. The performances of these estimators are investigated and compared by simulation, which manifests that the weight conditional least squares perform better than the conditional least squares. Finally, the COVID-19 data of severe cases in China and severe cases imported from outside China are analyzed to demonstrate the practical relevance of the model.

Suggested Citation

  • Xiufang Liu & Wenzheng Yin & Wenkun Zhang & Huaping Chen, 2024. "A mixed INAR(p) model with serially dependent innovation with application to some COVID-19 data," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 53(24), pages 8819-8847, December.
    • Handle: RePEc:taf:lstaxx:v:53:y:2024:i:24:p:8819-8847
    DOI: 10.1080/03610926.2023.2300305
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