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Composite likelihood expectation–maximization algorithm for the first-order multivariate integer-valued autoregressive model with multivariate mixture distributions

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  • Yu, Weiyang
  • Zheng, Haitao

Abstract

In this paper, we develops a estimation framework for first-order multivariate integer-valued autoregressive (MINAR(1)) models with multivariate Poisson-lognormal (MPL) or multivariate geometric-logitnormal (MGL) innovations. Owing to the structural complexity of the MINAR(1) framework and the latent dependence in MPL/MGL innovation processes, the likelihood functions involve summations and integrals. Traditional expectation–maximization (EM) algorithms face prohibitive computational demands as model dimensionality increases. To address this, we propose a composite likelihood expectation–maximization (CLEM) algorithm that strategically combines composite likelihood with the EM algorithm, reducing the computational burden. Furthermore, we implement a Cholesky parameterization for the covariance matrix to ensure positive definiteness. Through comprehensive Monte Carlo simulations, we demonstrate the performance of CLEM algorithm. Finally, we validate the method’s practical utility by analyzing a real-world dataset.

Suggested Citation

  • Yu, Weiyang & Zheng, Haitao, 2025. "Composite likelihood expectation–maximization algorithm for the first-order multivariate integer-valued autoregressive model with multivariate mixture distributions," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 237(C), pages 167-187.
    • Handle: RePEc:eee:matcom:v:237:y:2025:i:c:p:167-187
    DOI: 10.1016/j.matcom.2025.04.017
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    References listed on IDEAS

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