What is it about?
Non-Gaussian spatial responses are usually modeled using spatial generalized linear mixed model with spatial random effects. We proposed a new algorithm to estimation and prediction of this model.
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Why is it important?
The likelihood function of spatial generalized linear mixed models cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. In this work we introduced an approximate method that is fast and deterministic, using no sampling-based strategies.
Perspectives
In my opinion, in this paper is proposed a good idea that can be generalized to other models, such as the additive spatial generalized linear mixed models.
Fatemeh Hosseini
Semnan University
Read the Original
This page is a summary of: Approximate composite marginal likelihood inference in spatial generalized linear mixed models, Journal of Applied Statistics, August 2018, Taylor & Francis,
DOI: 10.1080/02664763.2018.1506020.
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