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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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This paper is concerned about the concept os asymmetry. The different types of asymmetry for univariate and multivariate distributions have introduces been considered as well as some of usual asymmetry criteria. A brief overview of method for adding the capability of modeling asymmetry to a symmetry distribution is also a secondary purpose of this paper.

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In this paper some new methods whitch very recently have been introduced for parameter estimation and variable selection in regression models are reviewd. Furthermore , we simulate several models in order to evaluate the performance of these methods under diffrent situation. At last we compare the performance of these methods with that of the regular traditional variable selection methods such as the forward selection and ridge regression.

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In this paper the exact determination of the distribution of stopping variable, the moment and risk of sequential estimator of the failure rate of exponential distribution, under convex boundary is obtained. The corresponding Poisson Process is used to derive the exact distribution of stopping variable of sequential estimator of the failure rate. In the end the exact values of mean and risk of sequential estimator of the failure rate is given in a table.

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Regression is one of the most important statistical tools in data analysis and study of the relationship between predictive variables and the response variable. in most issues, regression models and decision tress only can show the main effects of predictor variables on the response and considering interactions between variables does not exceed of two way and ultimately three-way, due to complexity of such interactions. To consider such interactions in the regression models, instead of individual variables in the model, we can construct a combination of them and use this combination as a new independent variable into the model Logic regression is a generalized regression and classification method that in this model, predictive variables are Boolean combinations that are made of the original binary variables. Annealing algorithm is used to find such combinations and their coefficients. randomization test or “null model test” is an overall test for signal in the data.also, cross-validation test can be used to determine the size of the logic tree model with the best predictive capability. As an example, we applied Logic Regression to predict diabetes in TLGS study.

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Seeking the optimal design with a given number of runs is a main problem in fractional factorial designs(FFDs). Resolution of a design is the most widely usage criterion, which is introduced by Box and Hunter(1961), used to be employed to regular FFDs. The resolution criterion is extended to non-regular FFG, called the generalized resolution criterion. This criterion is providing the idea of generalized minimum aberration criterion in non-regular designs. In this paper, we present these criteria and illustrate the advantages of non-regular designs in estimating the factorial effects with a smaller number of runs than that of regular FFDs. Some examples will be given.

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The sample autocorrelation function (acf) of a stationary process has played a central statistical role in traditional time series analysis, where the assumption is made that the marginal distribution has a second moment. Now, the classical methods based on acf are not applicable in heavy tailed modeling. Using the codifference function as dependence measure for such processes be shown it be as a new tool for order identification of stable moving average processes. Based on the empirical characteristic function, we propose a consistent estimator of the codifference function. In addition, we derive the limiting distribution. Finally, simulation study shows the method is good.

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