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As in recent years the scientific productivity about ISI database and other related database have been increased, it is eligible for researchers of Statistics in Iran to know more about these journals and their statues in ISI database. In this study with the use of bibliometric methods, we have reviewed the status of Statistics and Probability . From all nations around the world, these are only 12 countries whitch are active in publishing these 80 journals. Finding also show that England and USA are the most active countries in publishing Statistics journals. Each of these two countries publish 24 journals and both stands at the first rank in this regard. We also found that out of 80 Statistics journals in ISI database, 71 titles are published in English language and only 9 journals are published in other languages.

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‎Tree models represent a new and innovative way of analyzing large data sets by dividing predictor space into simpler areas‎. ‎Bayesian Additive Regression Trees model‎, ‎a model that we explain in this article‎, ‎uses a totality of trees in its structure‎, ‎since the combination of several trees from a tree only has a higher accuracy‎.

‎Then‎, ‎this model is a tree-based model and a nonparametric model that uses general aggregation methods‎, ‎and boosting algorithms in particular and in fact is extension of the classification and Regression Tree methods in which the decision tree exists in the structure of these methods‎.

‎In this method‎, ‎on the parameters of the model sum of tree and put regular prior then use the boosting algorithms for analysis‎. ‎In this paper‎, ‎first the Bayesian Additive Regression Trees model is introduced and then applied in survival analysis of lung cancer patients‎.

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Spatial count data is usually found in most sciences such as environmental science, meteorology, geology and medicine. Spatial generalized linear models based on Poisson (Poisson-lognormal spatial model) and binomial (binomial-logitnormal spatial model) distributions are often used to analyze discrete count data in which spatial correlation is observed. The likelihood function of these models is complex as analytic and so computation. The Bayesian approach using Monte Carlo Markov chain algorithms can be a solution to fit these models, although there are usually problems with low sample acceptance rates and long runtime to implement the algorithms. An appropriate solution is to use the Hamilton (hybrid) Monte Carlo algorithm
in The Bayesian approach. In this paper, the new Hamilton (hybrid) Monte Carlo method for Bayesian analysis of spatial count models on air pollution data in Tehran is studied. Also, the two common Monte Carlo algorithms such as the Markov chain (Gibbs and Metropolis-Hastings) and Langevin-Hastings are used to apply the complete Bayesian approach to the data modeling. Finally, an appropriate approach to data analysis and forecasting in all points of the city is introduced with the diagnostic criteria.

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Recently with science and technology development, data with functional nature are easy to collect. Hence, statistical analysis of such data is of great importance. Similar to multivariate analysis, linear combinations of random variables have a key role in functional analysis. The role of Theory of Reproducing Kernel Hilbert Spaces is very important in this content. In this paper we study a general concept of Fisher’s linear discriminant analysis that extends the classical multivariate method to the case functional data. A bijective map is used to link a second order process to the reproducing kernel Hilbert space, generated by its within class covariance kernel. Finally a real data set related to Iranian weather data collected in 2008 is also treated.

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Spatial generalized linear mixed models are used commonly for modeling discrete spatial responses. In this models the spatial correlation of the data is considered as spatial latent variables. For simplicity, it is usually assumed in these models that spatial latent variables are normally distributed. An incorrect normality assumption may leads to inaccurate results and is therefore erroneous. In this paper we model the spaial latent variables in a general random field, namely the closed skew Gaussian random field which is more flexible and includes the Gaussian random field. We propose a new algorithm for maximum likelihood estimates of the parameters. A key ingredient in our algorithm is using a Hamiltonian Monte Carlo version of the EM algorithm. The performance of the proposed model and algorithm is presented through a simulation study.

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Spatial generalized linear mixed model is commonly used to model Non-Gaussian data and the spatial correlation of the data is modelled by latent variables. In this paper, latent variables are modeled using a stationary skew Gaussian random field and a new algorithm based on composite marginal likelihood is presented. The performance of this stationary random field in the model and the proposed algorithm is implemented in a simulation example.