Volume 13, Issue 2, June 2014, Pages 151 - 161
Bayesian Prediction in Clipped GLG Random Field Using Slice Sampling
Majid Jafari Khaledi, Hamidreza Zareifard, Firoozeh Rivaz
Majid Jafari Khaledi
Received 29 September 2013, Accepted 20 January 2014, Available Online 31 March 2014.
- https://doi.org/10.2991/jsta.2014.13.2.5How to use a DOI?
- Binary spatial data; Bayesian latent model; Heavy tail; Slice sampling
- By assuming that an underlying Gaussian-Log Gaussian (GLG) random field clipped to yield binary spatial data, we propose a new model which provides flexibility in capturing the effects of heavy tail in latent variables. For our analysis, we adopt a Bayesian framework and develop a Markov chain Monte Carlo (MCMC) algorithm to carry out the posterior computations. Specifically, we introduce auxiliary variables and employ the slice sampling method to simulate from the full conditional distribution of components which does not define a standard probability distribution. Then, the predictive distribution at unsampled sites is approximated based on acquired samples. Finally, we illustrate our methodology considering simulation and real data sets.
- Open Access
- This is an open access article distributed under the CC BY-NC license.
Cite this article
TY - JOUR AU - Majid Jafari Khaledi AU - Hamidreza Zareifard AU - Firoozeh Rivaz PY - 2014 DA - 2014/03/31 TI - Bayesian Prediction in Clipped GLG Random Field Using Slice Sampling JO - Journal of Statistical Theory and Applications SP - 151 EP - 161 VL - 13 IS - 2 SN - 2214-1766 UR - https://doi.org/10.2991/jsta.2014.13.2.5 DO - https://doi.org/10.2991/jsta.2014.13.2.5 ID - Khaledi2014 ER -