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الرئيسية > Analyzing of Diabetes Data Usi...>الوصف

Analyzing of Diabetes Data Using SIR-Based Methods

المصدر: مجلة القادسية للعلوم الإدارية والاقتصادية
الناشر: جامعة القادسية - كلية الادارة والاقتصاد
المؤلف الرئيسي: Alkenani, Ali (Author)
مؤلفين آخرين: Abdulkadhim, Mohamed (Co-Author)
المجلد/العدد: مج23, ع4
محكمة: نعم
الدولة: العراق
التاريخ الميلادي: 2021
الصفحات: 224 - 231
ISSN: 1816-9171
رقم MD: 1235372
نوع المحتوى: بحوث ومقالات
اللغة: الإنجليزية
قواعد المعلومات: EcoLink
مواضيع:
الداء السكري | تحليل البيانات | الإحصاء التحليلي
رابط المحتوى:

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المستخلص: 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.
ISSN: 1816-9171

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