Bayesian Classification with First Order Moving Average Sources | ||||
| The Egyptian Statistical Journal | ||||
| Article 11, Volume 36, Issue 2, December 1992, Page 317-325 PDF (10.94 MB) | ||||
| Document Type: Original Article | ||||
| DOI: 10.21608/esju.1992.314869 | ||||
 View on SCiNiTO | ||||
| Authors | ||||
| Ahmed Haroun* ; Samir Shaarawy | ||||
| Cairo University, Egypt | ||||
| Abstract | ||||
| The main objective of this paper is to develop a convenient Bayesian procedure that can be used to assign a univariate time series realization to one of several first order moving average sources, with unknown coefficients, that share a common unknown precision. The foundation of the proposed procedure is to develop the marginal posterior mass function of a classification vector using an approximate conditional likelihood function. A time series realization is assigned to that first order moving average process with the largest posterior probability. A comprehensive simulation study with two sources is carried out to demonstrate the performance of the proposed procedure and to check its adequacy in handling the classification problems. | ||||
| Keywords | ||||
| Moving Average Processes; Classification; Posterior Mass Function; Bayesian Analysis | ||||
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