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                                       Details van artikel 6 van 19 gevonden artikelen
 
 
  Bayesian classification with multivariate autoregressive sources that might have different orders
 
 
Titel: Bayesian classification with multivariate autoregressive sources that might have different orders
Auteur: Shaarawy, Samir M.
Almahmeed, Mohammad A.
Verschenen in: Communications in statistics
Paginering: Jaargang 24 (1995) nr. 3 pagina's 567-581
Jaar: 1995
Inhoud: The main objective of this paper is to develop an exact Bayesian technique that can be used to assign a multivariate time series realization to one of several autoregressive sources, with unknown coefficients and precision, that might have different orders. The foundation of the proposed technique is to develop the posterior mass function of a classification vector, in an easy form, using the conditional likelihood function. A multivariate time series realization is assigned to the multivariate autoregressive source with the largest posterior probability. A simulation study, with uniform prior mass function, is carried out to demonstrate the performance of the proposed technique and to test its adequacy in handling the multivariate classification problems. The analysis of the numerical results supports the adequacy of the proposed technique in solving the classification problems with multivariate autoregressive sources.
Uitgever: Taylor & Francis
Bronbestand: Elektronische Wetenschappelijke Tijdschriften
 
 

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