Biased Estimators in Poisson Regression Model in the Presence of Multicollinearity: A Subject Review
| المصدر: | مجلة القادسية للعلوم الإدارية والاقتصادية |
|---|---|
| الناشر: | جامعة القادسية - كلية الادارة والاقتصاد |
| المؤلف الرئيسي: | Algamal, Zakariya Yahya (Author) |
| المجلد/العدد: | مج20, ع1 |
| محكمة: | نعم |
| الدولة: |
العراق |
| التاريخ الميلادي: |
2018
|
| الصفحات: | 36 - 43 |
| ISSN: |
1816-9171 |
| رقم MD: | 919980 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | EcoLink |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Multicollinearity | Biased Estimator | Poisson Regression Model | Monte Carlo Simulation
|
| رابط المحتوى: |
الناشر لهذه المادة لم يسمح بإتاحتها. |
| المستخلص: |
The presence of high correlation among predictors in regression mode has undesirable effects on the regression estimating. In the literature, there are several available biased methods to overcome this issue. The Poisson regression model (PRM) is a special model from the generalized linear models. The PRM is a well-known model in research application when the response variable under the study is count data. Numerous biased estimators for overcoming the multicollinearity in Poisson regression model have been proposed in the literature using different theories. An overview of recent biased methods for PRM is provided. A comparison among these biased estimators allows us to gain an insight into their performance. Simulation and real data application results show that the Liu-type estimator is comparable to other estimators. |
|---|---|
| ISSN: |
1816-9171 |
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