سناء جبار طعمة

Abstract This study introduces a new hierarchical formulation of the Bayesian Lasso by incorporating the Scale Mixture of Normals mixing with Rayleigh (BSCNRMIXING prior) into the Tobit Quantile Regression (Tobit Q Regression) framework. The BSCNRMIXING prior is proposed as a promising alternative to the widely used Scale Mixture of Normals mixing with Rayleigh (BSCNRMIXING prior), providing enhanced effectiveness in achieving simultaneous coefficient estimation and variable selection within the Bayesian Lasso paradigm. For Bayesian inference, Gibbs sampling schemes are derived for the full conditional posterior distributions. The proposed methodology is rigorously examined through comprehensive simulation experiments and an application to real data, with comparative analyses against established approaches, thereby highlighting its efficiency, stability, and robustness.

With the rapid development that the world has witnessed in all scientific fields, data analysis has become more complex due to the large size of collected data that needs to be analyzed. Because of that the data analyst needs a powerful tool to deal with such that kind of data and this is why the dimension reduction methods have developed. In this paper, we focuses on minimum average variance estimation as powerful dimension reduction tools to analysis of ischemic stroke for sample of patients under some covariates. The aim of this paper is to determine the most important covariates that affect the size of ischemic stroke as response variable.