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Bayesian identification of double seasonal autoregressive models / Dina Ali Bekhet ; Supervised Mohamed Ali Ismail , Rasha M. Elsouda

By: Contributor(s): Material type: TextTextLanguage: English Publication details: Cairo : Dina Ali Bekhet , 2017Description: 70 P. : charts ; 25cmOther title:
  • التحديد البيزى لنماذج الانحدار الذاتى الموسمية المزدوجة [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 main goal of this study is to solve the identification problem for double seasonal autoregressive models from Bayesian point of view. Two Bayesian identification techniques are employed; namely the direct and the indirect. A 585 simulation studies are conducted to assess the efficiency of both proposed Bayesian techniques. They are also compared with non Bayesian one (Akaike Information Criterion: AIC) taking in consideration the affected factors. These factors include the model order (p, P₁, P₂) the sampling variance (x⁻¹), the series length (n), the seasonal periods (s₁, s₂), and the model coefficients ({u0264}). Results showed that the indirect technique is superior to direct one. Finally the Bayesian identification approaches are applied on six real time series data
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Thesis Thesis قاعة الرسائل الجامعية - الدور الاول المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.03.01.M.Sc.2017.Di.B (Browse shelf(Opens below)) Not for loan 01010110074384000
CD - Rom CD - Rom مخـــزن الرســائل الجـــامعية - البدروم المكتبة المركزبة الجديدة - جامعة القاهرة Cai01.03.01.M.Sc.2017.Di.B (Browse shelf(Opens below)) 74384.CD Not for loan 01020110074384000

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

The main goal of this study is to solve the identification problem for double seasonal autoregressive models from Bayesian point of view. Two Bayesian identification techniques are employed; namely the direct and the indirect. A 585 simulation studies are conducted to assess the efficiency of both proposed Bayesian techniques. They are also compared with non Bayesian one (Akaike Information Criterion: AIC) taking in consideration the affected factors. These factors include the model order (p, P₁, P₂) the sampling variance (x⁻¹), the series length (n), the seasonal periods (s₁, s₂), and the model coefficients ({u0264}). Results showed that the indirect technique is superior to direct one. Finally the Bayesian identification approaches are applied on six real time series data

Issued also as CD

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