Generalized Skew Laplace Random Fields: Bayesian Spatial Prediction for Skew and Heavy Tailed Data
- https://doi.org/10.2991/jsta.d.210111.001How to use a DOI?
- Bayesian spatial prediction, Multivariate generalized skew Laplace distribution, Metropolis–Hastings, Gibbs sampling
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.
- © 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 -