A Bayesian framework for finite mixture models to deal with model selection and the selection of the number of mixture components simultaneously is presented. For that purpose, a feasible reversible jump Markov Chain Monte Carlo algorithm is proposed to model each component as a sparse regression model. This approach is made robust to outliers by using a prior that induces heavy tails and works well under multicollinearity and with high-dimensional data. Finally, the framework is applied to cross-sectional data investigating early warning indicators. The results reveal two distinct country groups for which estimated effects of vulnerability indicators vary considerably.

提出了一种有限混合模型的贝叶斯框架，以同时处理模型选择和混合组分数量的选择。为此，提出了一种可行的可逆跳跃马尔可夫链蒙特卡罗算法，将每个组件建模为稀疏回归模型。通过使用先验的方法使该方法对异常值具有鲁棒性，该先验方法会导致出现大量尾巴，并且在多重共线性和高维数据下可以很好地工作。最后，该框架被应用于调查预警指标的横截面数据。结果揭示了两个不同的国家组，其脆弱性指标的估计影响差异很大。