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.
Haroun, A., & Shaarawy, S. (1992). Bayesian Classification with First Order Moving Average Sources. The Egyptian Statistical Journal, 36(2), 317-325. doi: 10.21608/esju.1992.314869
MLA
Ahmed Haroun; Samir Shaarawy. "Bayesian Classification with First Order Moving Average Sources", The Egyptian Statistical Journal, 36, 2, 1992, 317-325. doi: 10.21608/esju.1992.314869
HARVARD
Haroun, A., Shaarawy, S. (1992). 'Bayesian Classification with First Order Moving Average Sources', The Egyptian Statistical Journal, 36(2), pp. 317-325. doi: 10.21608/esju.1992.314869
VANCOUVER
Haroun, A., Shaarawy, S. Bayesian Classification with First Order Moving Average Sources. The Egyptian Statistical Journal, 1992; 36(2): 317-325. doi: 10.21608/esju.1992.314869
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