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:: Volume 25, Issue 1 (1-2021) ::
Andishe 2021, 25(1): 9-15 Back to browse issues page
A Comparison of Algorithms for Maximum Likelihood Estimation of Spatial GLM models
Fatemeh Hossini * , Omid Karimi
Semnan University
Abstract:   (1838 Views)

In spatial generalized linear mixed models, spatial correlation is assumed by adding normal latent variables to the model. In these models because of the non-Gaussian spatial response and the presence of latent variables, the likelihood function cannot usually be given in a closed form, thus the maximum likelihood approach is very challenging. The main purpose of this paper is to introduce two new algorithms for the maximum likelihood estimations of parameters and to compare them in terms of speed and accuracy with existing algorithms. The presented algorithms are applied to a simulation study and their performances are compared.

Keywords: Spatial Correlation, Spatial Generalized Linear Mixed Model, Maximum Likelihood Estimation
Full-Text [PDF 1161 kb]   (850 Downloads)    
Type of Study: Research | Subject: Special
Received: 2020/06/16 | Accepted: 2021/01/20 | Published: 2021/01/29
References
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Hossini F, Karimi O. A Comparison of Algorithms for Maximum Likelihood Estimation of Spatial GLM models. Andishe 2021; 25 (1) :9-15
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Volume 25, Issue 1 (1-2021) Back to browse issues page
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