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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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Abstract: Depending on the type of distribution, estimation of parameters are not sometimes simple in practice. In particular, this is the case for Birnbaum-Saunders distribution (BS). In this article, we present four different methods for estimating the parameters of a BS distribution. First, a simple graphical technique, analogous to probability plotting, is used to estimate the parameters and check for goodness-of-fit of failure times following a Birnbaum-Saunders distribution. Then, the maximum likelihood estimators and a modification of the moment estimators of a two-parameter Birnbaum–Saunders distribution are discussed. Finally, The jackknife technique is considered as another method which is appropriate for the small sample size case. Monte Carlo simulation is also used to compare the performance of all these estimators.

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Beliefs are the result of uncertainty. Sometimes uncertainty is because of a random process and sometimes the result of lack of information. In the past, the only solution in situations of uncertainty has been the probability theory. But the past few decades, various theories of other variables and systems are put forward for the systems with no adequate and accurate information. One of these approaches is the theory of evidence. In recent years, the theory Dempster-Shafer or the theory of belief functions as a generalization of the probability theory, which makes the display of different levels of information, from complete certainty to total ignorance. According to the theory of belief, the belief function presents a mathematical probability for the use of mental judgments.A model for persenting belief functions is the transferable belief model. This model is basically similar to the Bayesian model. In this model, beliefs are at two levels, one credal level in which beliefs are accepted by belief functions that are quantified, and other is the pignistic level in which beliefs can be used for decision making and quantified by probability functions. This model differs the Bayesian model regarding creedal level the Bayesian model does not have this level.

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The two-parameter Birnbaum–Saunders (BS) distribution was originally proposed as a failure time distribution
for fatigue failure caused under cyclic loading. BS model is a positively skewed statistical distribution which has
received great attention in recent decades. Several extensions of this distribution with various degrees of skewness,
kurtosis and modality are considered. In particular, a generalized version of this model was derived based on symmetrical
distributions in the real line named the generalized BS (GBS) distribution. In this article, we propose a
new family of life distributions, generated from elliptically contoured distributions, and the density and some of its
properties are obtained. Explicit expressions for the density of a number of specific elliptical distributions, such as
Pearson type VII, t, Cauchy, Kotz type, normal, Laplace and logistic are found. Another generalization of the BS
distribution is also presented using skew-elliptical distribution which makes its symmetry more flexible. Finally,
some examples are provided to illustrate application of the distribution.

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In many issues of statistical modeling, the common assumption is that observations are normally distributed. In
many real data applications, however, the true distribution is deviated from the normal. Thus, the main concern of
most recent studies on analyzing data is to construct and the use of alternative distributions. In this regard, new
classes of distributions such as slash and skew-slash family of distributions have been introduced .This has been the
main concern of many researcher’s investigations in recent decades. Slash distribution, as a heavy tailed symmetric
distribution, is known in robust studies. But since , in empirical examples, there are many situations where symmetric
distributions are not suitable for fitting the data study of skew distributions has become of particular importance.In
this paper we introduce skew-slash distribution and study their properties. Finally, some applications to several real
data sets are illustrated in order to show the importance of the distribution in regression models.

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 ‎Censored samples are discussed in experiments of life-testing; i.e‎. ‎whenever the experimenter does not observe the failure times of all units placed on a life test‎. ‎In recent years‎, ‎inference based on censored sampling is considered‎, ‎so that about the parameters of various distributions such as ‎normal‎, ‎exponential‎, ‎gamma‎, ‎Rayleigh‎, ‎Weibull‎, ‎log normal‎, ‎inverse Gaussian‎, ‎logistic‎, ‎Laplace‎, ‎and Pareto‎, ‎has been inferred based on censored sampling‎.

‎In this paper‎, ‎a procedure for exact hypothesis testing and obtaining confidence interval for mean of the exponential distribution under Type-I progressive hybrid censoring is proposed‎. ‎Then‎, ‎performance of the proposed confidence interval is evaluated using simulation‎. ‎Finally‎, ‎the proposed procedures are performed on a data set‎.

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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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In this study, the interval estimations are prosed for the functions of the parameter in exponential lifetimes, when interval
censoring is used. Optimal monitoring time and simulation studies are examined as well as the applicability of the topics.

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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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In probability theory, a random variable (vector) is divided into discrete, absolutely continuous, singular continuous, and a mixture of them. Absolutely discrete and continuous random variables (vectors) have been extensively studied in various probability and statistics books. However, less attention has been paid to the issue of singular continuous distributions and mixture distributions, part of which is singular continuous. In this article, an example of ‎singular‎ random vectors is given. Also, examples of mixture random vectors are presented whose distribution function is a convex linear combination of discrete, absolutely ‎continuous,‎ and continuous distribution functions.

 

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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.

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Risk means a situation in which there is a possibility of deviation from a predicted result. Insurance is one of the methods of risk exposure that leads to the transfer of all or part of the risk from the insured to the insurer. Insurance policies are usually classified into two types: personal and non-life (non-life) insurance. According to this classification, any insurance policy that deals with the health of the insured person or persons is a personal insurance policy, otherwise it will be a nonlife insurance policy. Many insurances in the insurance industry are classified as non-life insurances. Fire, automobile, engineering, shipping, oil and energy insurances are examples of these insurances. Explanation and calculation of three issues in risk models are very important: the ruin probability, the time of ruin and the amount of ruin. In this article, the main and well-known results that have been obtained so far in the field of non-life insurance; Emphasizing the possibility of ruin, it is given along with various examples.