%0 Journal Article
%T Inference of Spatial Generalized Linear Mixed Models using Integrated Laplace Nested Approximation
%J Journal of Advanced Mathematical Modeling
%I Shahid Chamran University of Ahvaz
%Z 2251-8088
%A Hosseini, Fatemeh
%A Karimi, Omid
%A Mohammad Karimi, Monavar
%D 2014
%\ 08/23/2014
%V 4
%N 1
%P 55-69
%! Inference of Spatial Generalized Linear Mixed Models using Integrated Laplace Nested Approximation
%K Spatial generalized linear mixed model
%K Monte Carlo algorthms
%K Approximation Baysian approach
%R
%X Spatial generalized linear mixed models are used for modeling geostatistical discrete spatial responses and spatial correlation of the data is considered via latent variables. The most important interest in these models is estimation of the model parameters and the prediction of the latent variables. In this paper, first a prediction method is presented and then, Bayesian approach and MCMC algorithms are intrpretation. Since these models are complex and in the Bayes inference of these models, are used Monte Carlo sampling, computation time is long. The Approximatin Baysian methods are considered for solving this problem. Finally, the proposed methods are applied to a case study on rainfall data observed in the weather stations of Semnan in 1391.
%U https://jamm.scu.ac.ir/article_10772_dd0c1fedfed8ba59ab30b0e82fa1778a.pdf