TY - JOUR JF - jsci JO - VL - 14 IS - 2 PY - 2014 Y1 - 2014/7/01 TI - Modelling of Spatial Extreme Values with Random Field and Copula Function TT - مدل بندی مقادیر کرانگین فضایی با میدان تصادفی و تابع مفصل N2 - In this article a spatial model is presented for extreme values with marginal generalized extreme value (GEV) distribution. The spatial model would be able to capture the multi-scale spatial dependencies. The small scale dependencies in this model is modeled by means of copula function and then in a hierarchical manner a random field is related to location parameters of marginal GEV distributions in order to account for large scale dependencies. Bayesian inference of presented model is accomplished by offered Markov chain Monte Carlo (MCMC) design, which consisted of Gibbs sampler, random walk Metropolis-Hastings and adaptive independence sampler algorithms. In proposed MCMC design the vector of location parameters is updated simultaneously based on devised multivariate proposal distribution. Also, we attain Bayesian spatial prediction by approximation of the predictive distribution. Finally, the estimation of model parameters and possibilities for capturing and separation of multi-scale spatial dependencies are investigated in a simulation example and analysis of wind speed extremes. SP - 127 EP - 140 AU - Mahmoudian, Behzad AU - Mohammadzadeh Darrodi, Mohsen AD - Department of Statistics, Tarbiat Modares University, P.O.Box 14115-134 KW - Extreme values KW - Generalized extreme value distribution KW - Copula function KW - Random field KW - Adaptive independence sampler KW - Random walk Metropolis-Hastings algorithm UR - http://jsci.khu.ac.ir/article-1-1499-en.html ER -