New Bayesian Variable Selection for Binary Quantile Regression
| المصدر: | مجلة القادسية للعلوم الإدارية والاقتصادية |
|---|---|
| الناشر: | جامعة القادسية - كلية الادارة والاقتصاد |
| المؤلف الرئيسي: | Alshaybawee, Taha Hussein (Author) |
| مؤلفين آخرين: | Fahem, Mariam Qais (Co-Author) |
| المجلد/العدد: | مج25, ع3 |
| محكمة: | نعم |
| الدولة: |
العراق |
| التاريخ الميلادي: |
2023
|
| الصفحات: | 302 - 311 |
| ISSN: |
1816-9171 |
| رقم MD: | 1438006 |
| نوع المحتوى: | بحوث ومقالات |
| اللغة: | الإنجليزية |
| قواعد المعلومات: | EcoLink |
| مواضيع: | |
| كلمات المؤلف المفتاحية: |
Quantile Regression | Bayesian Inference | Posterior Distribution | Binary Data
|
| رابط المحتوى: |
الناشر لهذه المادة لم يسمح بإتاحتها. |
| المستخلص: |
Quantile regression extends the mean regression model to conditional quantiles of the response variable (Koenker and Bassett, 1978; Koenker, 2005). As a result of its capacity to take advantage of all available data in the analysis, the Bayesian method of analysis has become extremely popular. We proposed a novel estimation method and selected variables. Using Bayesian methodology for binary quantile regression. On the basis of the recently proposed scale mixture of normal distribution mixing Rayleigh density representation for Laplace distribution prior density of the parameters vector, new hierarchical models have been developed. Simulation Scenarios and actual data are used to evaluate the efficacy of the new method in comparison to two existing methods. |
|---|---|
| ISSN: |
1816-9171 |
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