In spatial generalized linear mixed models, spatial correlation is considered by adding latent variables to the model. In these models, since the spatial response variable is non-Gaussian and due to the presence of latent variables, the likelihood function usually does not have a closed form, and therefore the maximum likelihood approach for parameter estimation faces challenges. The main objective of this paper is to introduce two new algorithms for obtaining maximum likelihood estimates of parameters and to compare them with existing algorithms in terms of speed and accuracy. The introduced algorithms are applied on a simulated dataset and their performance is compared.
Hosseini, F. & Karimi, O. (2020). Comparison of several algorithms for maximum likelihood estimation of spatial generalized linear mixed models. Andishe_ye Amari, 25(1), 9-15.
MLA
Hosseini, F., & Karimi, O. "Comparison of several algorithms for maximum likelihood estimation of spatial generalized linear mixed models", Andishe_ye Amari, 25, 1, 2020, 9-15.
HARVARD
Hosseini F., Karimi O. (2020). 'Comparison of several algorithms for maximum likelihood estimation of spatial generalized linear mixed models', Andishe_ye Amari, 25(1), pp. 9-15.
CHICAGO
F. Hosseini & O. Karimi, "Comparison of several algorithms for maximum likelihood estimation of spatial generalized linear mixed models," Andishe_ye Amari, 25 1 (2020): 9-15,
VANCOUVER
Hosseini F., Karimi O. Comparison of several algorithms for maximum likelihood estimation of spatial generalized linear mixed models. Andishe_ye Amari. 2020;25(1):9-15 (In Persian).
