عناصر مشابهة
Estimating the Linear Regression Model in High-Dimensional Data and Collinearity
| المصدر: | المجلة العلمية لقطاع كليات التجارة |
|---|---|
| الناشر: |
جامعة الأزهر - كلية التجارة
|
| المؤلف الرئيسي: | |
| مؤلفين آخرين: | , |
| المجلد/العدد: | ع24 |
| محكمة: | نعم |
| الدولة: | مصر |
| التاريخ الميلادي: | 2020 |
| الصفحات: | 70 - 98 |
| DOI: | 10.21608/jsfc.2020.248230 |
| ISSN: | 2636-3674 |
| رقم MD: | 1301830 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | English |
| قواعد المعلومات: | EcoLink |
| مواضيع: | |
| رابط المحتوى: |
| المستخلص: | This paper is concerned with introducing the most used penalized regression methods, including ridge regression (RR), least absolute shrinkage and selection operator (LASSO), and elastic net (EN) regression for estimating the linear regression model. These models are used in two cases low and high-dimensional data when data is contain outliers when the explanatory variables have collinearity among them. The Monte Carlo simulation study is conducted to evaluate and compare the performance of these estimators. The simulation results indicate that the obtained estimators using EN are efficient and reliable than the other estimators. |
|---|
