Estimating General Linear Regression model by Using Sure Independence Screening SIS Method under High Dimensional Data with Application

Abstract

Estimating the general linear regression model when there is a high dimensions data understudy is very important topic to study and analyze, due the problems that appear when the dimension of data exceeds like multicollinearity, the study aims at using different adaptive penalized function to estimate the general linear regression model by using Sure Independence Screening SIS, in addition other method were chosen which is Ridge Regression. Data were collected that represent Social Deprivation Index SDI in Iraq from different governments of Iraq, moreover; simulation data were held and comparison were made to choose the better estimating method by using Mean Square Errors MSE.