Bayesian Inference for Seasonal ARMA Models: A Gibbs Sampling Approach

Abstract

This paper introduces a fast, easy and accurate Gibbs sampling algorithm to develop a Bayesian inference for a multiplicative seasonal autoregressive moving average (SARMA) model. The proposed algorithm uses values generated from normal and inverse gamma distributions and does not involve any Metropolis-Hastings generation. Simulated examples and a real data set are used to illustrate the proposed algorithm.

Publication
In Proceedings of the 10th islamic countries conference on statistical sciences (ICCXS-X)
Date
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Ayman A. Amin
Assistant Professor of Statistics
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