Detection and modeling of change-points in time-series can be considerably challenging. In this paper we approach this problem by incorporating the class of Dynamic Generalized Linear Models (DGLM) into the well know class of Product Partition Models (PPM). This new methodology, that we call DGLM-PPM, extends the PPM to distributions within the Exponential Family while also retaining the flexibility of the DGLM class. It also provides a framework for Bayesian multiple change-point detection in dynamic regression models. Inference on the DGLM-PPM follow the steps of evolution and updating of the DGLM class. A Gibbs Sampler scheme with an Adaptive Rejection Metropolis Sampling (ARMS) step appended is used to compute posterior estimates of the relevant quantities. A simulation study shows that the proposed model provides reasonable estimates of the dynamic parameters and also assigns high change-point probabilities to the breaks introduced in the artificial data generated for this work. We also present a real life data example that highlights the superiority of the DGLM-PPM over the conventional DGLM in both in-sample and out-of-sample goodness of fit measures.
翻译:在本文中,我们通过将动态通用线性线性模型(DGLM)的类别纳入熟知的产品分配模型(PPM)的类别来解决这一问题。我们称之为DGLM-PPM的这一新方法将PPM扩大至DGLM-PPM的分布范围,同时保留DGLM等级的灵活性。它还为Bayesian多点变化点动态回归模型探测提供了一个框架。DGLM-PPPM的推论遵循DGLM等级的演变和更新步骤。附加的Gibbs采样器计划带有适应性拒绝大宗采样(ARMS)步骤,用来计算相关数量的事后估计。模拟研究表明,拟议的模型提供了对动态参数的合理估计,并对为这项工作生成的人工数据中的断裂提供了高变化点概率。我们还举了一个真实生命数据的例子,突出DGLM-PPM-PRODM措施优于常规的DGMM-MMM-SDGM措施的优势。