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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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‎A simultaneous confidence band gives useful information on the reasonable range of the unknown regression model‎. ‎In this note‎, ‎when the predictor variables are constrained to a special ellipsoidal region‎, ‎hyperbolic and constant width confidence bonds for a multiple linear regression model are compared under the minimum volome confidence set (MVCS) criterion‎. ‎The size of one speical angle that determines the size of the predictor variable region is used to find out which band is better than the other‎. ‎When the angle and consquently the size of the predictor variable region is small‎, ‎the constant width band is better than the hyperbolic band‎.

‎When the angle hence the size of the predictor variable regoin is large‎, ‎the hyperbolic band is considerably better than the constant width band‎.

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Today, applying statistics in other sciences, including medical sciences, has become very common. Researchers consider optimal design as a tool to increase the efficiency of experiments.
Pharmacokinetics is particularly important in the medical sciences as a branch of pharmacology that studies the performance of drugs in living organisms.
This study aims to introduce optimal designs for models in pharmacokinetic studies. The models used in this paper are known as nonlinear models in the statistical literature. These models depend on specific parameters based on pharmacological factors and time as predictor variables.
Optimal designs are obtained based on functions of the Fisher information matrix. These functions are known as optimal criteria. In this paper, we consider two criteria, A- and E-optimality. Based on these two criteria, locally optimal designs are obtained for the considered models.

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Today, the diagnosis of diseases using artificial intelligence and machine learning algorithms are of great importance, because by using the data available in the study field of the desired disease, useful information and results can be obtained that reduce the occurrence of many deaths. Among these diseases, we can mention the diagnosis of diabetes, which has spread today due to the growth of urban life and the decrease in people's activity. So, it is very important to know whether a person is suffering from diabetes or not. In this article, the data set related to the information of people who have done the diabetes diagnosis test is used, this information is related to 520 people. People are classified into two groups based on whether their diabetes test result is positive or not, and Bayesian classification methods such as Bayesian Support Vector Machine, Naive Bayes, CNK and CatBoost ensemble classification method have been used to conclude which of these The methods can have a better ability to analyze the data and also to compare these methods use accuracy, precision, F1-score, recall, ROC diagram.