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Bayesian forecasting of double seasonal autoregressive processes / Mahmoud Mohamed Ibrahim Abdellatief ; Supervised Mohamed Ali Ismail

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Mahmoud Mohamed Ibrahim Abdellatief , 2017Description: 71 P. : charts ; 25cmOther title:
  • التنبؤ البيزي لنماذج الانحدار الذاتي ثنائية الموسمية [Added title page title]
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Dissertation note: Thesis (M.Sc.) - Cairo University - Faculty of Economics and Political Science - Department of Statistics Summary: The study develops a Bayesian forecasting for the multiplicative Double Seasonal Autoregressive models. Two different approaches are employed; the first is an approximate Bayesian forecasting and the second one is Gibbs sampling approach. A normal inverse gamma prior is used because it is a conjugate class. The adequacy of both proposed Bayesian approaches is checked using four simulated examples and a real data set. Empirical results indicate the accuracy of Bayesian forecasts where these Bayesian forecasts lie within the credible intervals
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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.2017.Ma.B (Browse shelf(Opens below)) Not for loan 01010110073568000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.03.01.M.Sc.2017.Ma.B (Browse shelf(Opens below)) 73568.CD Not for loan 01020110073568000

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

The study develops a Bayesian forecasting for the multiplicative Double Seasonal Autoregressive models. Two different approaches are employed; the first is an approximate Bayesian forecasting and the second one is Gibbs sampling approach. A normal inverse gamma prior is used because it is a conjugate class. The adequacy of both proposed Bayesian approaches is checked using four simulated examples and a real data set. Empirical results indicate the accuracy of Bayesian forecasts where these Bayesian forecasts lie within the credible intervals

Issued also as CD

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