The Bayesian method is firstly applied for the selection of the best subset for the double-threshold moving average (DTMA) model. The Markov chain Monte Carlo (MCMC) techniques and the stochastic search variable selection (SSVS) method are used to identify the best subset model from a very large number of possible models. Simulation experiments show that the proposed method is feasible and efficient, despite the complexity being increased by the large number of subsets, and the uncertainty of the threshold and delay variables. Our method is illustrated by real data analysis on the Yen-Dollar exchange rate.

中文翻译：

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基于贝叶斯方法的双阈值移动平均模型子集选择

贝叶斯方法首先应用于双阈值移动平均（DTMA）模型的最佳子集的选择。马尔可夫链蒙特卡罗 (MCMC) 技术和随机搜索变量选择 (SSVS) 方法用于从大量可能的模型中识别最佳子集模型。仿真实验表明，尽管大量子集增加了复杂度，以及阈值和延迟变量的不确定性，该方法是可行和有效的。我们的方法通过对日元兑美元汇率的真实数据分析来说明。