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.
M.Shaarawy, S., E.A.Soliman, E., & M.Elsouda, R. (2011). Bayesian Identification of Seasonal Moving Average Models. The Egyptian Statistical Journal, 55(1), 40-53. doi: 10.21608/esju.2011.314310
MLA
Samir M.Shaarawy; Emad E.A.Soliman; Rasha M.Elsouda. "Bayesian Identification of Seasonal Moving Average Models", The Egyptian Statistical Journal, 55, 1, 2011, 40-53. doi: 10.21608/esju.2011.314310
HARVARD
M.Shaarawy, S., E.A.Soliman, E., M.Elsouda, R. (2011). 'Bayesian Identification of Seasonal Moving Average Models', The Egyptian Statistical Journal, 55(1), pp. 40-53. doi: 10.21608/esju.2011.314310
VANCOUVER
M.Shaarawy, S., E.A.Soliman, E., M.Elsouda, R. Bayesian Identification of Seasonal Moving Average Models. The Egyptian Statistical Journal, 2011; 55(1): 40-53. doi: 10.21608/esju.2011.314310
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