Bayesian Identification of Seasonal Moving Average Models | ||||
| The Egyptian Statistical Journal | ||||
| Article 4, Volume 55, Issue 1, June 2011, Page 40-53 PDF (17.35 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/esju.2011.314310 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Samir M.Shaarawy1; Emad E.A.Soliman2; Rasha M.Elsouda3 | ||||
| 1Faculty of Science, Umm El-Qura University, KSA | ||||
| 2King Abdul-Aziz University, KSA | ||||
| 3Cairo University, Egypt | ||||
| Abstract | ||||
| This study approaches the Bayesian identification of seasonal moving average processes using an approximate likelihood function and a normal gamma prior density. The marginal posterior probability mass function of the model orders is developed in a convenient form. Then one may investigate the posterior probabilities over the grid of the orders and choose the orders combination with the highest probability to solve the identification problem. A comprehensive simulation study is carried out to demonstrate the performance of the proposed procedure and check its adequacy in handling the identification problem. In addition, the proposed Bayesian procedure is compared with the AIC automatic technique. The numerical results support the adequacy of using the proposed procedure in solving the identification problem of seasonal moving average processes. | ||||
| Keywords | ||||
| Identification; Seasonal Moving Average Processes; Automatic Techniques; Normal Gamma Density; Posterior Probability Mass Function | ||||
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