Spatial generalized linear mixed model is commonly used to model Non-Gaussian data and the spatial correlation of the data is modelled by latent variables. In this paper, latent variables are modeled using a stationary skew Gaussian random field and a new algorithm based on composite marginal likelihood is presented. The performance of this stationary random field in the model and the proposed algorithm is implemented in a simulation example.
Hosseini, F. & Karimi, O. (2022). OnEstimation for Spatial Generalized Linear Mixed Models
using a Stationary Skew Gaussian Random Field. Andishe_ye Amari, 27(1), 73-79.
MLA
Hosseini, F., & Karimi, O. "OnEstimation for Spatial Generalized Linear Mixed Models
using a Stationary Skew Gaussian Random Field", Andishe_ye Amari, 27, 1, 2022, 73-79.
HARVARD
Hosseini F., Karimi O. (2022). 'OnEstimation for Spatial Generalized Linear Mixed Models
using a Stationary Skew Gaussian Random Field', Andishe_ye Amari, 27(1), pp. 73-79.
CHICAGO
F. Hosseini & O. Karimi, "OnEstimation for Spatial Generalized Linear Mixed Models
using a Stationary Skew Gaussian Random Field," Andishe_ye Amari, 27 1 (2022): 73-79,
VANCOUVER
Hosseini F., Karimi O. OnEstimation for Spatial Generalized Linear Mixed Models
using a Stationary Skew Gaussian Random Field. Andishe_ye Amari. 2022;27(1):73-79 (In Persian).
