Volume 20, Issue 1, March 2021, Pages 76 - 85
Generalized Skew Laplace Random Fields: Bayesian Spatial Prediction for Skew and Heavy Tailed Data
Authors
Mohammad Mehdi Saber1, *, Alireza Nematollahi2, Mohsen Mohammadzadeh3
1Department of Statistics, Higher Education Center of Eghlid, Eghlid, Iran
2Department of Statistics, Shiraz University, Shiraz, Iran
3Department of Statistics, Tarbiat Modares University, Tehran, Iran
*Corresponding author. Email: mmsaber@eghlid.ac.ir
Corresponding Author
Mohammad Mehdi Saber
Received 15 January 2019, Accepted 4 December 2020, Available Online 20 January 2021.
- DOI
- 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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TY - JOUR AU - Mohammad Mehdi Saber AU - Alireza Nematollahi AU - Mohsen Mohammadzadeh PY - 2021 DA - 2021/01/20 TI - Generalized Skew Laplace Random Fields: Bayesian Spatial Prediction for Skew and Heavy Tailed Data JO - Journal of Statistical Theory and Applications SP - 76 EP - 85 VL - 20 IS - 1 SN - 2214-1766 UR - https://doi.org/10.2991/jsta.d.210111.001 DO - 10.2991/jsta.d.210111.001 ID - Saber2021 ER -