Shrinkage Estimators in Inverse Gaussian Regression Model: Subject Review
| المصدر: | المجلة العراقية للعلوم الإحصائية |
|---|---|
| الناشر: | جامعة الموصل - كلية علوم الحاسوب والرياضيات |
| المؤلف الرئيسي: | Yonis, Farah Abdulghani (Author) |
| مؤلفين آخرين: | Othman, Rafal Adeeb (Co-Author) |
| المجلد/العدد: | ع35 |
| محكمة: | نعم |
| الدولة: |
العراق |
| التاريخ الميلادي: |
2022
|
| الصفحات: | 72 - 82 |
| ISSN: |
1680-855X |
| رقم MD: | 1318195 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | EcoLink |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Multicollinearity | Biased Estimator | Inverse Gaussian Regression Model | Monte Carlo Simulation
|
| رابط المحتوى: |
الناشر لهذه المادة لم يسمح بإتاحتها. |
| المستخلص: |
The presence of the high correlation among predictors in regression modeling has undesirable effects on the regression estimating. There are several available biased methods to overcome this issue. The inverse Gaussian regression model (IGRM) is a special model from the generalized linear models. The IGRM is a well-known model in research application when the response variable under the study is skewed data. Numerous biased estimators for overcoming the multicollinearity in IGRM have been proposed in the literature using different theories. An overview of recent biased methods for IGRM is provided. A comparison among these biased estimators allows us to gain an insight into their performance. |
|---|---|
| ISSN: |
1680-855X |
عناصر مشابهة
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