Bayesian Inference for Seasonal ARMA Model: A Gibbs Sampling Approach

Abstract

This thesis 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
Master’s thesis, Statistics Department, Faculty of Economics and Political Science, Cairo University, Egypt
Date
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Ayman A. Amin
Assistant Professor of Statistics
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