Journal of Statistical Theory and Applications

In Press, Uncorrected Proof, Available Online: 20 January 2021

Generalized Skew Laplace Random Fields: Bayesian Spatial Prediction for Skew and Heavy Tailed Data

Authors
Mohammad Mehdi Saber1, *, Alireza Nematollahi2, Mohsen Mohammadzadeh3
1 Department of Statistics, Higher Education Center of Eghlid, Eghlid, Iran
2 Department of Statistics, Shiraz University, Shiraz, Iran
3 Department of Statistics, Tarbiat Modares University, Tehran, Iran
*Corresponding author. Email: mmsaber@eghlid.ac.ir
Corresponding Author
Mohammad Mehdi Saber
Available Online 20 January 2021.
DOI
https://doi.org/10.2991/jsta.d.210111.001How to use a DOI?
Keywords
Bayesian spatial prediction, Multivariate generalized skew Laplace distribution, Metropolis–Hastings, Gibbs sampling
Abstract

Earlier works on spatial prediction issue often assume that the spatial data are realization of Gaussian random field. However, this assumption is not applicable to the skewed and kurtosis distributed data. The closed skew normal distribution has been used in these circumstances. As another alternative, we apply generalized skew Laplace distributions for defining a skew and heavy tailed random field for Bayesian prediction. Simulation study and a real problem are then applied to evaluate the performance of this model.

Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

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Journal
Journal of Statistical Theory and Applications
Publication Date
2021/01
ISSN (Online)
2214-1766
ISSN (Print)
1538-7887
DOI
https://doi.org/10.2991/jsta.d.210111.001How to use a DOI?
Copyright
© 2021 The Authors. Published by Atlantis Press B.V.
Open Access
This is an open access article distributed under the CC BY-NC 4.0 license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - JOUR
AU  - Mohammad Mehdi Saber
AU  - Alireza Nematollahi
AU  - Mohsen Mohammadzadeh
PY  - 2021
DA  - 2021/01
TI  - Generalized Skew Laplace Random Fields: Bayesian Spatial Prediction for Skew and Heavy Tailed Data
JO  - Journal of Statistical Theory and Applications
SN  - 2214-1766
UR  - https://doi.org/10.2991/jsta.d.210111.001
DO  - https://doi.org/10.2991/jsta.d.210111.001
ID  - Saber2021
ER  -