Using SIR-based methods to determine the number of explanatory
variables effecting on the blood pressure data
Prof. Dr. Ali Alkenani, Dheyaa Alaboudi
Department of Statistics, College of Administration and Economics, University of Al-Qadisiyah
| Corresponding Author: Dheyaa Alaboudi |
| Abstract : In some multiple regression applications, the number of predictors has become large, and for this reason, the Sufficient Dimension Reduction (SDR) theory (Cook, 1998) has received much attention. The idea of Sufficient Dimension Reduction (SDR) is to replace X with a low-dimensional orthogonal projection on the subspaces () without Loss of information about the distribution X without assuming any specific model. The target of the SDR is the central subspace many methods have been worked out to find and one such method is the inverse regression (SIR) slices (Li, 1991). Applied in different fields, SIR has proven robust for dimension reduction (DR) approach and is effective in handling high dimensional (HD) data and sufficient tools to deal with dimension reduction (DR) in conditional regression (Li and Yin, 2008). However, it does produce linear combinations (LCs) for all the original predictors. As a result, interpretation of SIR estimates can be difficult and sometimes misleading. This paper will use methods that combine SIR work with the Lasso method. Ni et al. (2005) A note on shrinkage sliced inverse regression (SH-SIR), Li and Yin (2008) Sliced Inverse Regression with Regularizations (RSIR) and Lin et al.( (2018) Sparse Sliced Inverse Regression Via Lasso (SIR-L) methods in analysis sample data for high blood pressure and the factors affecting it. |
