INAR approximation of bivariate linear birth and death process

Zezhun Chen*, Angelos Dassios, George Tzougas

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

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Abstract

In this paper, we propose a new type of univariate and bivariate Integer-valued autoregressive model of order one (INAR(1)) to approximate univariate and bivariate linear birth and death process with constant rates. Under a specific parametric setting, the dynamic of transition probabilities and probability generating function of INAR(1) will converge to that of birth and death process as the length of subintervals goes to 0. Due to the simplicity of Markov structure, maximum likelihood estimation is feasible for INAR(1) model, which is not the case for bivariate and multivariate birth and death process. This means that the statistical inference of bivariate birth and death process can be achieved via the maximum likelihood estimation of a bivariate INAR(1) model.

Original languageEnglish
Pages (from-to)459-497
Number of pages39
JournalStatistical Inference for Stochastic Processes
Volume26
Issue number3
Early online date15 May 2023
DOIs
Publication statusPublished - Oct 2023

Keywords

  • Bivariate birth and death
  • Convergence in distribution
  • Discrete approximation
  • Integer-valued autoregressive of order one
  • Linear birth and death

ASJC Scopus subject areas

  • Statistics and Probability

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