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Graphical Laplace-approximated maximum likelihood estimation: approximated likelihood inference for network data analysis

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 نشر من قبل Chaonan Jiang
 تاريخ النشر 2021
  مجال البحث الاحصاء الرياضي
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We derive Laplace-approximated maximum likelihood estimators (GLAMLEs) of parameters in our Graph Generalized Linear Latent Variable Models. Then, we study the statistical properties of GLAMLEs when the number of nodes $n_V$ and the observed times of a graph denoted by $K$ diverge to infinity. Finally, we display the estimation results in a Monte Carlo simulation considering different numbers of latent variables. Besides, we make a comparison between Laplace and variational approximations for inference of our model.



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