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

07/13/2021 ∙ by Chaonan Jiang, et al. ∙ University of Geneva 0

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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