Dimension reduction analysis of Ischemic stroke by using MAVE and SMAVE methods

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. And the results indicated that the SMAVE method is the best method for prediction of the future ischemic stoke volume.