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Spatial INAR(1,1) model based on mixing Pegram and binomial thinning operators with fitting striga counts

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  • Alireza Ghodsi
  • Hassan S. Bakouch

Abstract

In this article, we propose a new spatial integer-valued model to model the spatial count data on a two-dimensional regular grid using mixing Pegram and binomial thinning operators and name it as first-order spatial non-negative integer-valued autoregressive model with mixing Pegram and binomial thinning operators, in short Sp-MPBTINAR(1,1) model. Some of its properties have been derived, and the estimation of the parameters of the model are disscussed. Finally the numerical results are presented. The simulation studies showed that the conditional maximum likelihood (CML) estimators are consistent. The empirical studies also showed that the Sp-MPBTINAR(1,1) model with geometric innovation distribution has a better fit to the data considered here.

Suggested Citation

  • Alireza Ghodsi & Hassan S. Bakouch, 2025. "Spatial INAR(1,1) model based on mixing Pegram and binomial thinning operators with fitting striga counts," Communications in Statistics - Theory and Methods, Taylor & Francis Journals, vol. 54(21), pages 6988-6996, November.
    • Handle: RePEc:taf:lstaxx:v:54:y:2025:i:21:p:6988-6996
    DOI: 10.1080/03610926.2025.2465647
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