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The dimension-wise quadrature estimation of dynamic latent variable models for count data

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  • Bianconcini, Silvia
  • Cagnone, Silvia

Abstract

When dynamic latent variable models are specified for discrete and/or mixed observations, problems related to the integration of the likelihood function arise since analytical solutions do not exist. A recently developed dimension-wise quadrature is applied to deal with these likelihoods with high-dimensional integrals. A comparison is performed with the pairwise likelihood method, one of the most often used remedies. Both a real data application and a simulation study show the superior performance of the dimension-wise quadrature with respect to the pairwise likelihood in estimating the parameters of the latent autoregressive process.

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  • Bianconcini, Silvia & Cagnone, Silvia, 2023. "The dimension-wise quadrature estimation of dynamic latent variable models for count data," Computational Statistics & Data Analysis, Elsevier, vol. 177(C).
    • Handle: RePEc:eee:csdana:v:177:y:2023:i:c:s0167947322001657
    DOI: 10.1016/j.csda.2022.107585
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    References listed on IDEAS

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    Cited by:

    1. Aghabazaz, Zeynab & Kazemi, Iraj, 2023. "Under-reported time-varying MINAR(1) process for modeling multivariate count series," Computational Statistics & Data Analysis, Elsevier, vol. 188(C).

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