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Quantile regression via a modified weighted goal programming approach with application / Heba Abdelqader Ahmed Erfan ; Supervised Ramadan Hamed Mohamed , Mahmoud Mostafa Rashwan

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Heba Abdelqader Ahmed Erfan , 2021Description: 64 P. : charts ; 25cmOther title:
  • الإنحدار الجزيئى يإستخدام أسلوب معدل لبرمجة الهدف المرجحة مع التطبيق [Added title page title]
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  • Issued also as CD
Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Economics and Political Science - Department of Statistics Summary: Recently, quantile regression has received great attention in data analysis because it captures the effect of the independent variables on the whole distribution of the dependent variable, unlike traditional linear regression which provides only one estimate as a representative of this effect for the entire distribution. In this thesis, a modified weighted goal programming model is proposed to obtain the estimates of the quantile regression parameters with less computational effort than the original model of the quantile regression. The proposed model saves more than half the CPU time used for computing quantile regression estimates compared to the original model. A simulation study is performed to assess the performance of the proposed model. Also, the quantile regression is applied to the students{u2019} scores dataset of Faculty of Economics and Political Science at Cairo University to determine the effect of the COVID-19 pandemic on the students{u2019} performance
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Item type Current library Home library Call number Copy number Status Date due Barcode
Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.03.01.M.Sc.2021.He.Q (Browse shelf(Opens below)) Not for loan 01010110084239000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.03.01.M.Sc.2021.He.Q (Browse shelf(Opens below)) 84239.CD Not for loan 01020110084239000

Thesis (M.Sc.) - Cairo University - Faculty of Economics and Political Science - Department of Statistics

Recently, quantile regression has received great attention in data analysis because it captures the effect of the independent variables on the whole distribution of the dependent variable, unlike traditional linear regression which provides only one estimate as a representative of this effect for the entire distribution. In this thesis, a modified weighted goal programming model is proposed to obtain the estimates of the quantile regression parameters with less computational effort than the original model of the quantile regression. The proposed model saves more than half the CPU time used for computing quantile regression estimates compared to the original model. A simulation study is performed to assess the performance of the proposed model. Also, the quantile regression is applied to the students{u2019} scores dataset of Faculty of Economics and Political Science at Cairo University to determine the effect of the COVID-19 pandemic on the students{u2019} performance

Issued also as CD

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