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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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This paper is devoted to computing the sample size of binomial distribution with Bayesian approach. The quadratic loss function is considered and three criterions are applied to obtain p-tolerance regions with the lowest posterior loss. These criterions are: average length, average coverage and worst outcome.

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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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