Events and Meetings of Italian Statistical Society, Advances in Latent Variables - Methods, Models and Applications

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Sparse Nonparametric Graphical Models for Random Effect Distribution in GLMMs
Sara Viviani, Marco Alfò, Pierpaolo Brutti

Last modified: 2013-06-14

Abstract


A generalized linear mixed model with a nonparametric distribution for the random effect is proposed. In the context of nonparametric graphical models, we take advantage of the nonparanormal approach to build a flexible latent, individual specific structure for the longitudinal profiles. The nonparanormal method is particularly appealing since it acts on transformations of multivariate non-Gaussian random variables, and assumes that these transformations are multivariate Gaussian. Moreover, it is particularly convenient to handle the joint distribution for high dimensional variables. In the case of generalized linear mixed models, the normality assumption for the random effects may be too restrictive to represent the between subject distribution, especially when the longitudinal response is non-Gaussian.

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