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-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.
DOI
10.21608/esju.2011.314310
Keywords
identification, Seasonal Moving Average Processes, Automatic Techniques, Normal gamma density, Posterior probability mass function
Link
https://esju.journals.ekb.eg/article_314310.html
Detail API
https://esju.journals.ekb.eg/service?article_code=314310
Publication Title
The Egyptian Statistical Journal
Publication Link
https://esju.journals.ekb.eg/
MainTitle
Bayesian Identification of Seasonal Moving Average Models