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:: Volume 26, Issue 1 (12-2021) ::
Andishe 2021, 26(1): 37-46 Back to browse issues page
Hamiltonian Monte Carlo Methods for Analysing Skew GLM models
Fatemeh Hosseini *, Omid Karimi
Semnan University
Abstract:   (813 Views)

Spatial generalized linear mixed models are used commonly for modeling discrete spatial responses. In this models the spatial correlation of the data is considered as spatial latent variables. For simplicity, it is usually assumed in these models that spatial latent variables are normally distributed. An incorrect normality assumption may leads to inaccurate results and is therefore erroneous. In this paper we model the spaial latent variables in a general random field, namely the closed skew Gaussian random field which is more flexible and includes the Gaussian random field. We propose a new algorithm for maximum likelihood estimates of the parameters. A key ingredient in our algorithm is using a Hamiltonian Monte Carlo version of the EM algorithm. The performance of the proposed model and algorithm is presented through a simulation study.

Keywords: Spatial Generalized Linear Mixed Model, Hamiltonian Monte Carlo Algorithm, Closed Skew Gaussian Random Field.
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Type of Study: Research | Subject: Special
Received: 2021/07/28 | Accepted: 2021/11/22 | Published: 2021/12/1
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Hosseini F, Karimi O. Hamiltonian Monte Carlo Methods for Analysing Skew GLM models. Andishe. 2021; 26 (1) :37-46
URL: http://andisheyeamari.irstat.ir/article-1-864-en.html

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Volume 26, Issue 1 (12-2021) Back to browse issues page
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