AU - Mohammadzadeh Darrodi, Mohsen TI - Estimation of spatial generalized linear mixed models with closed skew normal latent variables PT - JOURNAL ARTICLE TA - jsci JN - jsci VO - 12 VI - 1 IP - 1 4099 - http://jsci.khu.ac.ir/article-1-1433-en.html 4100 - http://jsci.khu.ac.ir/article-1-1433-en.pdf SO - jsci 1 ABĀ  - Spatial generalized linear mixed models are usually used for modeling non-Gaussian and discrete spatial responses. In these models, spatial correlation of the data can be considered via latent variables. Estimation of the latent variables at the sampled locations, the model parameters and the prediction of the latent variables at un-sampled locations are of the most important interest in SGLMM. Often the normal assumption for latent variables is considered just for convenient in practice. Although this assumption simplifies the calculations, in practice, it is not necessarily true or possible to be tested. In this paper, a closed skew normal distribution is proposed for the spatial latent variables. This distribution includes the normal distribution and also remains closed under linear conditioning and marginalization. In these models, likelihood function cannot usually be given in a closed form and maximum likelihood estimations may be computationally prohibitive. In this paper, for maximum likelihood estimation of the model parameters and predictions of latent variables, an approximate algorithm is introduced that is faster than the former method. The performance of the proposed model and algorithm are illustrated through a simulation study. CP - IRAN IN - LG - eng PB - jsci PG - 305 PT - S YR - 2012