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:: Volume 14, Issue 2 (3-2018) ::
JSRI 2018, 14(2): 157-169 Back to browse issues page
Parameter Estimation in Spatial Generalized Linear Mixed Models with Skew Gaussian Random Effects using Laplace Approximation
Seyed Reza Hosseini Shojaei , Yadollah Waghei 1, Mohsen Mohammadzadeh
1- , ywaghei@birjand.ac.ir
Abstract:   (3644 Views)
 Spatial generalized linear mixed models are used commonly for modelling non-Gaussian discrete spatial responses. We present an algorithm for parameter estimation of the models using Laplace approximation of likelihood function. In these models, the spatial correlation structure of data is carried out by random effects or latent variables. In most spatial analysis, it is assumed that random effects have Gaussian distribution, but the assumption is questionable. This assumption is replaced in the present work, using a skew Gaussian distribution for the latent variables, which is more flexible and includes Gaussian distribution. We examine the proposed method using a real discrete data set.
 
Keywords: Laplace approximation, multivariate skew Gaussian, random effects, SGLM, spatial data.
Full-Text [PDF 1270 kb]   (1861 Downloads)    
Type of Study: Research | Subject: General
Received: 2016/08/5 | Accepted: 2017/10/16 | Published: 2018/03/17
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Hosseini Shojaei S R, Waghei Y, Mohammadzadeh M. Parameter Estimation in Spatial Generalized Linear Mixed Models with Skew Gaussian Random Effects using Laplace Approximation. JSRI 2018; 14 (2) :157-169
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Volume 14, Issue 2 (3-2018) Back to browse issues page
مجله‌ی پژوهش‌های آماری ایران Journal of Statistical Research of Iran JSRI
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