Abstract. Within the context of likelihood and Bayes approaches to inference in autoregressive moving-average (ARMA) time series models, previous ideas on parameter transformation and numerical integration for implementing Bayesian procedures are reviewed. Some novel transformation ideas are introduced and their role in an efficient numerical integration approach is examined. Some comparisons of the effectivesness of different numerical integration strategies are made.

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Volume13, Issue4

July 1992

Pages 327-343

REPARAMETRIZATION ASPECTS OF NUMERICAL BAYESIAN METHODOLOGY FOR AUTOREGRESSIVE MOVING-AVERAGE MODELS

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REPARAMETRIZATION ASPECTS OF NUMERICAL BAYESIAN METHODOLOGY FOR AUTOREGRESSIVE MOVING-AVERAGE MODELS

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