Abstract
We propose an estimation procedure to incorporate non-separable spatiotemporal correlation into a generalized linear mixed model. The motivation of this paper is from a study of enterovirus infection with spatial-temporal correlation. The proposed method underlying a working estimating equation comes from a generalization of weighted least squares approaches. With an iterative two-stage estimation procedure, we may address the non-identifiability problem caused by latent random effects. Under certain regularity conditions, we show that the proposed estimate has consistency and asymptotic normality for spatiotemporal data. We also conduct a model-based simulation and apply the method to the enterovirus data in Taiwan.
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