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Showing 3 results for Mohammadzadeh Darrodi

Mohsen Mohammadzadeh Darrodi, ,
Volume 12, Issue 1 (11-2012)
Abstract

Spatial generalized linear mixed models are usually used for modeling non-Gaussian and discrete spatial responses. In these models, spatial correlation of the data can be considered via latent variables. Estimation of the latent variables at the sampled locations, the model parameters and the prediction of the latent variables at un-sampled locations are of the most important interest in SGLMM. Often the normal assumption for latent variables is considered just for convenient in practice. Although this assumption simplifies the calculations, in practice, it is not necessarily true or possible to be tested. In this paper, a closed skew normal distribution is proposed for the spatial latent variables. This distribution includes the normal distribution and also remains closed under linear conditioning and marginalization. In these models, likelihood function cannot usually be given in a closed form and maximum likelihood estimations may be computationally prohibitive. In this paper, for maximum likelihood estimation of the model parameters and predictions of latent variables, an approximate algorithm is introduced that is faster than the former method. The performance of the proposed model and algorithm are illustrated through a simulation study.
Mehdi Omidi, Mohsen Mohammadzadeh Darrodi,
Volume 13, Issue 3 (11-2013)
Abstract

Copula functions are powerful tools for construction the multivariate distribution of dependent variables in terms of their marginal distributions. Each of these functions provide a model which represents all properties of the variables dependency. For spatial data analysis, the dependence structure of the data should be determined by using the multivariate distribution of the random field. In analysis of Spatio-temporal data it is also necessary to identify the relations between spatial and temporal structure of the data in terms of Spatio-temporal covariance function. Sometimes a separable Spatio-temporal covariance function is used for the ease of application, but in some applications this property is not realistic. In these cases it is required to use a non-separable Spatio-temporal covariance function. In this paper the role of copula functions in determination of joint distribution of a random field is considered and the properties of a valid spatial copula function are determined. Then a new valid spatial copula family is introduced. Next some spatial and nonseparable Spatio-temporal covariance functions are constructed by using these copula functions
Behzad Mahmoudian, Mohsen Mohammadzadeh Darrodi, ,
Volume 14, Issue 2 (7-2014)
Abstract

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.

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