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A full Bayesian analysis framework using scale-mixtures of normal distributions for seasonal autoregressive models, combined with stochastic search variable selection, enables accurate multi-step-ahead forecasts for time series that violate normality assumptions. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Full Bayesian analysis of seasonal autoregressive models under scale-mixtures of normal errors

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