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- W14803304 abstract "Non-Gaussian point-referenced spatial data are frequently modeled using generalized linear mixed models (GLMM) with location-specific random effects. Spatial dependence can be introduced in the covariance matrix of the random effects. Maximum likelihood-based or Bayesian estimation implemented via Markov chain Monte Carlo (MCMC) for such models is computationally demanding especially for large sample sizes because of the large number of random effects and the inversion of the covariance matrix involved in the likelihood. We review three fitting procedures, the Penalized Quasi Likelihood method, the MCMC, and the Sampling-Importance-Resampling method. They are assessed in terms of estimation accuracy, ease of implementation, and computational efficiency using a spatially structured dataset on infant mortality from Mali." @default.
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- W14803304 date "2003-11-01" @default.
- W14803304 modified "2023-09-26" @default.
- W14803304 title "Fitting Generalized Linear Mixed Models For Point-Referenced Spatial Data" @default.
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- W14803304 doi "https://doi.org/10.22237/jmasm/1067646180" @default.
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