论文标题
贝叶斯的混合回归自回旋模型涵盖完整参数空间的分析
Bayesian analysis of mixture autoregressive models covering the complete parameter space
论文作者
论文摘要
混合回旋（MAR）模型为模型时间序列提供了一种灵活的方式，该模型序列具有预测性分布，这些分布取决于该过程的最新历史，并能够适应不对称和多模式。贝叶斯对此类模型的推论提供了将估计模型中的不确定性纳入预测中的其他优势。与以前的方法不同，我们从MAR模型参数的后部分布中引入了一种新的采样方式，该方法允许覆盖模型的完整参数空间。我们还提出了一种重新标记算法来处理标签切换的后验。我们将新方法应用于模拟和真实的数据集，讨论我们的新方法的准确性和性能，以及与以前的研究相比其优势。还引入了使用MCMC输出预测的密度预测的想法。
Mixture autoregressive (MAR) models provide a flexible way to model time series with predictive distributions which depend on the recent history of the process and are able to accommodate asymmetry and multimodality. Bayesian inference for such models offers the additional advantage of incorporating the uncertainty in the estimated models into the predictions. We introduce a new way of sampling from the posterior distribution of the parameters of MAR models which allows for covering the complete parameter space of the models, unlike previous approaches. We also propose a relabelling algorithm to deal a posteriori with label switching. We apply our new method to simulated and real datasets, discuss the accuracy and performance of our new method, as well as its advantages over previous studies. The idea of density forecasting using MCMC output is also introduced.
