- Publisher: Al-Qadisiyah Journal for Administrative and Economic Sciences
- Available in: PDF
- DOI: 10.33916/23.4.2021/224-231
- Published: December 25, 2021

**Analyzing of diabetes data Using SIR-based methods**

Ali Alkenani

Mohamed Abdulkadhim

Al-Qadisiyah University – College of Administration and Economics

Corresponding Author: Mohamed Abdulkadhim

Abstract : The SDR received great attention in high-dimensional regressions. Assume Y is a response variable and

X=(x_1,…,x_p )^T is a predictor of p-dimensions. Without assuming any parametric model, the main idea of SDR is

to replace X with a Low-dimensional orthogonal P_s X to S while retaining information about the Y | X distribution.

The aim of SDR procedure is to find the central subspace S_(Y|X), and that S_(Y|X)is the intersection of all

subspaces such as Y╨X|P_s X. Where ╨ denotes independence. Therefore, P_β X excerpts all the information from

X about Y, where β is the base to S_(Y|X). (Cook, 1998).

There are several proposed methods for finding S_(Y|X), and one of the well-known methods is SIR (Li, 1991),

SIR is applied in several fields including economics, and bioinformatics. SIR faces difficulties in interpreting the

resulting estimates about SIR due to its production of linear combinations from all of the original predictors. To

improve the interpretation of SIR analysis, it is necessary to decrease the number of non-zero coefficients which are

also insignificant in the SIR directions.

The objective of our study is to reduce the number of nonzero coefficients in SIR directions for obtaining better

interpretability. Through combining some of the regularization methods with the SIR method to produce sparse and

accurate estimations.

in this paper will we employ methods that merge SIR work with the Lasso method. SSIR(Ni et al, 2005), RSIR (Li

and Yin, 2008), SIR-LASSO Lin et al.( (2018) methods in analyses sample data for diabetes